Automating the Extraction of User Model Information from ... - CiteSeerX

Final Thesis

Automating the Extraction of User Model Information from Consultation Dialogues by cand. inform. Dennis Maciuszek

Technical University of Braunschweig, Germany Examiner:

Nahid Shahmehri, Ph.D., Professor Supervisor:

Johan  Aberg, M.Sc.

Linkoping University, Sweden Department of Computer and Information Science Laboratory for Intelligent Information Systems November 9, 2001

2

3 LiTH-IDA-Ex-01/69 Automating the Extraction of User Model Information from Consultation Dialogues

4

5

Abstract This thesis addresses a natural language processing problem posed in the context of so-called Web assistant systems aka live help systems. A recent feature added to a growing number of Web sites, such systems oer user support via text chat with human assistants. To adapt consultation to the individual user, long-term information about his or her skills and interests is collected in a user model. So far, this updating of user models has been a task performed manually by the assistants. The thesis speci es, designs, implements, and evaluates a software component to automate the user model acquisition task. Text phrases containing information about user skills and interests are (1) automatically highlighted in the consultation dialogues and (2) associated with semantic user model concepts. A requirements speci cation points out the particularities of human-human text chat communication. The particularities are considered in the choice of two suitable and feasible information extraction approaches: one keyword based and one partial parsing approach. The partial parsing approach is being designed to ful l the requirements, and then implemented. An evaluation of performance indicates the resulting system is well suited to facilitate manual user modelling, but not a reliable basis for a full automation yet.

Keywords: information extraction, Web assistant sys-

tem, live help system, user model, consultation dialogue, text chat, conversational circumstances

Contents Abstract

5

Contents List of Figures List of Tables

6 8 9

Foreword 1 Introduction

10 11

2 Information in Consultation Dialogues

26

3 Approaches to Information Extraction

44

1.1 The Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2 Application Domain . . . . . . . . . . . . . . . . . . . . . . . 13 1.3 Towards a Solution . . . . . . . . . . . . . . . . . . . . . . . . 23 2.1 Dialogue Style . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.2 Grammar and Vocabulary . . . . . . . . . . . . . . . . . . . . 33 3.1 3.2 3.3 3.4 3.5

Choice of Approaches . . . . . Chosen Approaches . . . . . . Keyword Based Interpretation FASTUS Partial Parsing . . . Rejected Approaches . . . . .

. . . . .

. . . . .

. . . . .

. . . . .

4 Implementing Information Extraction

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

44 46 50 53 57

63

4.1 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.3 Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 6

CONTENTS

7

5 Results

109

6 Conclusions

122

Bibliography A Grammar Speci cation

130 134

5.1 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 5.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

6.1 Recapitulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 6.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . 124

A.1 Grammar Rules . . . . . . . . . . . . . . . . . . . . . . . . . . 134 A.2 Vocabulary List . . . . . . . . . . . . . . . . . . . . . . . . . . 177

List of Figures 1.1 1.2 1.3 1.4 1.5 1.6 1.7 2.1 2.2 4.1 4.2 4.3 4.4 4.5 4.6 5.1 5.2 5.3 5.4 6.1 6.2

Elfwood WIS index page . . . . . . . . . . WAS overview [ASM01] . . . . . . . . . . An Elfwood consultation dialogue log (1) . An Elfwood consultation dialogue log (2) . An Elfwood consultation dialogue log (3) . Elfwood user model attributes (1) . . . . . Elfwood user model attributes (2) . . . . . Communication model of Schulz von Thun \Two and a half" applicable IE tasks . . . Information extraction in the WAS . . . . UML class diagram: Chat language . . . . UML class diagram: Partial parsing . . . . Partial parsing phases . . . . . . . . . . . Partial parsing phases in practice (1) . . . Partial parsing phases in practice (2) . . . The testing applet . . . . . . . . . . . . . UML class diagram: Application . . . . . . Displayed extracted information . . . . . . UML class diagram: Evaluation . . . . . . A sub-ontology of user model attributes . A sub-ontology of parts of speech . . . . .

8

. . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . .

. 14 . 14 . 16 . 17 . 18 . 21 . 22 . 27 . 35 . 63 . 65 . 71 . 73 . 75 . 76 . 110 . 110 . 112 . 115 . 127 . 128

List of Tables 1.1 2.1 2.2 2.3 3.1 3.2 4.1 4.2 5.1 5.2

Previous dialogue analysis statistics [ASM01] . . . . . Components of the four conversational circumstances A fth conversational circumstance . . . . . . . . . . Speci cation dialogue analysis statistics . . . . . . . . Comparison of chosen IE approaches . . . . . . . . . Keyword based interpretation: Lexicon entries . . . . Tag syntax de nitions . . . . . . . . . . . . . . . . . Tag syntax examples . . . . . . . . . . . . . . . . . . Sample extraction results . . . . . . . . . . . . . . . . Measured system performance . . . . . . . . . . . . .

9

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. 23 . 28 . 30 . 39 . 48 . 51 . 95 . 96 . 113 . 119

Foreword This Master's thesis was written during a six months stay in Linkoping as a guest student from Technical University of Braunschweig in Germany. I wish to express my thanks to everybody involved in making this exchange possible, especially my family for their encouragement and support, and to Dr Spie, who sent me here. I would like to thank my supervisor Johan  Aberg and my examiner Nahid Shahmehri for a truly fascinating project. I was happy being able to work with Natural Language Processing, User Modelling { and not to forget \Elfwood". Thank you for being patient and helpful, when the work grew larger, and results were long in coming. The atmosphere at IISLAB was at the same time warm and professional. Besides my supervisor and examiner, I thank Cecile  Aberg and Govert Meuwis for valuable comments on the document. Finally, thanks to my friends in Germany and in Sweden, for everything you do { especially Brigitte, Christelle, Gerrit, Inna, and Leah. Everybody's names are in the \complex words" le, so that my program would recognise you. Linkoping in July 2001, Dennis Maciuszek

10

Chapter 1 Introduction This introduces the problem to be solved, as well as its theoretical and its practical background.

1.1 The Problem

Concepts The project falls in the area of user support in Web information

systems (WIS). A WIS in this context may be any kind of Web site that provides substantial amounts of structured information plus a number of user functions on the data. User support means helping people acquire the information they seek { be it by guiding them to the information online, or by personal consultation. A Web assistant system (WAS, cf [AS00]), also known as a live help system, is a user support component added to a WIS, consisting of both computer-based help functions and consultation by human assistants. These would be experts in the WIS's application domain and also familiar with Web site operations. Users engage in online consultation dialogues with the assistants, when seeking help beyond what is stored in the system's knowledge base. In our case that knowledge base is a hypertext list of frequently asked questions (FAQ) and their answers. Consultations in a WAS can be personalised and made run more eciently by the assistants modelling user traits of every person they assist (cf [ASM01]). During the textual chat { or afterwards, studying its log le { the assistant records the user's statements regarding personal interests, skills, and the like, inserting discovered items into a well de ned structure: The assistant manually extracts information from the dialogue into a user model. Automation of information extraction (IE; see [Cun99], [CL96], or [AI99]

11

12

CHAPTER 1. INTRODUCTION

for an introduction) in this case means having a computer system process the natural language (or actually chat language) dialogue. It spots pieces of relevant information and stores them in the user model.

Goals To spare the assistants tedious work, an IE system taking over the

extraction task in an existing WAS was to be developed. For obtaining an approach feasible within the scope of one thesis, restrictions had to be set on the undertaking. Instead of allowing cut-backs on accuracy, the IE task of nding and inserting user model information was divided into two steps. The rst step intends the information to be discovered and to be highlighted in the assistant's chat window. It is indicated which aspect(s) of the user model the information in a phrase associates with. Processing happens in \real time", ie chat line by chat line, not just on nished dialogues. The second step, following this thesis, would determine what values to insert, depending on the content of the discovered phrase. The values would automatically be added to the user model. Until then, assistants are only pointed to user information; they still need to insert it themselves. We would consider step one successful, if a considerable percentage of what a human analyst would extract (when studying log les) was discovered and reasonably associated by the implemented IE system. In addition, the rate of false ndings had to be low.

Motivation The introduction of user models in a WAS adds adaptivity

to the system, in the sense that consultation can be tailored to individual user needs. Assistants become more comfortable and ecient in their job. A previous eld study [ASM01] conducted by the Laboratory for Intelligent Information Systems (IISLAB) indicated that assistants participating in a WAS consider user models (especially with many items added) to be helpful in their counselling sessions ([ASM01], Table 2). Only rarely did they lead to wrong assumptions about a user. However, comments about the diculty of the extraction task were less euphoric (a mean of 5.79 on a scale of 1 = hard to 10 = easy, with a standard deviation of 2.94). Presumably, automatic creation of adequate user models would make Web assisting more comfortable and ecient. The assistant job becomes less stressful and time-consuming; the WIS company saves nancial resources. With numerous WIS running today and the vast dierences in users' interests, education and cultural background, the number of possible applications of user models, WAS, and adaptivity on the Web seems huge. Exploring automatic generation of user models should quicken the interests not only of

1.2. APPLICATION DOMAIN

13

WAS researchers: The method could be applied to many types of adaptive systems, eg automatically gaining models of student knowledge in adaptive learning environments with chat features. On the other hand, a practice of automatically analysing people's behaviour { not in general, but on a personal level { and electronically storing the gathered results raises dicult ethical and legal questions. IE can easily be exploited for selective advertising or espionage. In fact, much IE research has been funded by the US Defense Advanced Research Projects Agency (DARPA). At least, this is where we enter a grey area, and we should keep an eye on what our results are being applied to. For the protection of privacy, access to user model data ought to be restricted (eg to registered assistants only) and always be granted to the user in question. My personal interest in the project came from dierent angles. Studying Computer Science and Psychology, my main research interest lies in the application of Information Science methods to psychological questions, as in modelling and emulating cognitive processes. In an earlier project, I had been building an adaptive learning environment based on models of student knowledge and domain1 knowledge. Then, I had already been using some manual extraction method for discovering prerequisite relationships between knowledge items in tutorial hypermedia texts. Now, by showing a way to automatically extract user model information from natural language consultation dialogues, I hope to make a contribution to a more adaptive WWW. As a matter of fact, processing natural language has fascinated me since the very beginning of my interest in computing, which was in the heydays of so-called text adventure games. Writing Fantasy ction as a hobby, I was also curious to apply IE research to the popular \Elfwood" WIS. The following section describes that system.

1.2 Application Domain

WIS The Elfwood Web information system2 is basically a huge, noncom-

mercial archive of amateur artwork and literature in the Fantasy and Science Fiction genre. It features a number of supportive functions like keyword based search and guided tours covering related exhibits, as well as tutorial articles about how to draw and write. Figure 1.1 shows an excerpt of Elfwood's index screen. Special emphasis lies on interactivity; users can comment the exhibits and communicate via IRC chat or message boards. To contribute their works, 1 the eld of knowledge or information 2 http://elfwood.lysator.liu.se

CHAPTER 1. INTRODUCTION

14

Figure 1.1: Elfwood WIS index page

11 00 00 11 00 11 00 11

Answers Question

Support Router

User

QuestionAnswering System

11 00 00 11 00 11 00 11

View and Edit

Consultation dialogue Assistant

Figure 1.2: WAS overview [ASM01]

User Modelling Tool

1.2. APPLICATION DOMAIN

15

users join either one or several of Elfwood's topical branches as a member. The branches are Fantasy art (\Lothlorien"), Science Fiction art (\Zone 47"), and literature (\The Wyvern's Library").

WAS So far, a Web assistant component has been integrated in the Elfwood WIS only temporarily for study and evaluation purposes. Investigated issues were a system evaluation from the user's point of view [AS01c], the generation and retrieval of FAQ items for computer-based support [AS01b], and the eectiveness of user models in aiding human assistants [ASM01]. The system works as depicted in Figure 1.2. It is called, when a registered user sends in a question. Questions consist of a natural language query and a topic category. A support router clari es, if computer-based support is sucient, or if human assistance will be needed. First, it sends the question to a question answering system, which attempts to retrieve corresponding FAQ answers (cf [AS01b]). If the user decides these do not help, the support router establishes a text-based chat connection with a human assistant whose expertise pro le covers the question's topic category. A text chat window will pop up on each side. User and assistant can then freely debate the problem matter. While talking, the assistant views and edits the user's model via an always visible user modelling tool. The more counselling sessions a user takes, the more re ned her or his model gets, allowing more adapted and individual support. Aside from the user modelling data, log les of all help dialogue chats are being kept. In its rst three weeks evaluation run, Elfwood's WAS included 35 voluntary assistants serving one user at a time. Support was given on questions of art and literature creation, art and literature search, as well as operating functions for users and members. Consultation dialogues A sample dialogue log le, recorded during the

eld study, is displayed in Figures 1.3 { 1.5 (with names anonymised). Note that tags have automatically been inserted for structuring purposes; the actual conversation begins after . Previous lines give the question's topic category () and natural language query (). and enclose FAQ matching results. The consultation dialogue centres around a user's diculties with drawing backgrounds (thus art creation), leading to a lively discussion about one picture in her mind, concluding with the assistant oering useful inspiration { not something easily accomplished by a computer advice. An attentive assistant can notice several details in the chat dialogue which

CHAPTER 1. INTRODUCTION

16

W 2000-4-4 - 3 0:28:30 Art I'm having problems drawing backgrounds for my pictures, what should I do? 0.6101508 0.23864917 0.22824878 0.22593212 0.22298692 0.22279699 0.20678684 0.19983208 0.19673619 0.1842602 0:29:54 76070 0:30:31 X 0:30:41 0:30:43 hi 0:30:56 0:31:1 Hi. 0:31:15 May I ask your name first? 0:31:20 I can't think of any good ideas for backgrounds for my drawings.

Figure 1.3: An Elfwood consultation dialogue log (1)

1.2. APPLICATION DOMAIN

0:31:24 0:31:39 My user or name or my real first name? 0:31:46 oops, user name 0:32:9 Your real name.. and whether you are a member of elfwood or not 0:32:32 I'm not a member of elfwood, but my name is Y Z 0:32:35 0:32:55 allright Y, so what media are you using, pencil? 0:33:8 Yes. 0:33:20 0:33:57 and what do you like to draw or do you have a specific picture in mind? 0:34:21 I like drawing anime-style fairies and elves, or just normal anime humans. 0:34:40 0:36:8 ok.. let's start by thinking the picture through.. what is the character(s) doing.. and where is he/she/it 0:36:11 0:36:24 that should clear the location a bit 0:36:38 0:37:14 The character is a female fairy just kind of standing in a weird position, but she's just floating in the middle of nowhere since i have no background. 0:37:46 for example if you draw a faerie the obvious location would be a forest or a pond of some sort 0:37:55 0:38:8 or do you have a story behind the picture?

Figure 1.4: An Elfwood consultation dialogue log (2)

17

CHAPTER 1. INTRODUCTION

18

0:38:54 Nope, I don't have a story, I was just drawing. I want it in a forest, obviously, but I don't know what the character should be standing on or around. 0:39:4 0:39:42 ok.. what is she doing? Is she watching something from a high place or hiding from something lurking in the shadows? 0:39:46 0:39:54 or just being casual :) 0:40:10 just being casual. 0:40:16 0:41:54 The first thing that comes to mind is a path or a meadow.. actually it is not very important where she is standing as long as she fits into the environment 0:42:0 0:42:31 okay, thanks. 0:42:37 if it is just a portrait I would put her on a fallen tree trunk or on a path with a few trees surrounding her 0:42:41 0:42:53 that's a good idea. 0:43:11 Can I help you with anything else? 0:43:29 0:43:38 Nope, that's all. 0:43:53 Thanks. 0:44:8 it's sometimes hard to draw backgrounds but they are worth the effort.. often they make the picture more interesting, so good luck 0:44:20

Figure 1.5: An Elfwood consultation dialogue log (3)

1.2. APPLICATION DOMAIN

19

reveal information about the user: 1. She can't think of ideas for backgrounds. [; time 0:31:20] 2. She's not an Elfwood member. [time 0:32:32] 3. Her real full name [time 0:32:32] 4. She uses pencils. [time 0:33:8] 5. Her style is anime. [time 0:34:21] 6. She draws fairies, elves and humans. [time 0:34:21] This is the type of information we seek for our user model (skills, interests, personal information). How do we know she is female, except from the name? Some basic data { age, gender and country { are already being inserted into the user model upon registration. Nevertheless, an IE system should detect these, when they occur in a statement. Some user model information is easy to spot, like in the statement I like drawing anime-style fairies and elves [time 0:34:21]. Some is harder to get. The talk about the user's name, for instance, is extremely misleading. As chat dialogues tend to run asynchronously (delayed), the rst question about her name is followed by a repetition of the problem [time 0:31:20]. Only later she replies, asking for a more dierentiated question, oering her user name or real rst name [time 0:31:39] { misspelling the sentence and correcting it one line after [time 0:31:46]. In the end, she even gives her full name [time 0:32:32]. Furthermore, irrelevant information could be extracted. The phrase at time 0:38:54, I don't know what the character should be standing on or around., refers to a particular short term goal, not general knowledge (or absence of). Finally, extracted information can be wrong, when read out of context: i have no background [time 0:37:14]. Still, this happens to be one of the more comprehensible dialogues.

User model The established data structure for the Elfwood user model is

simple. There is no psychological or complex mathematical foundation (the reasons for this are given in [ASM01], Section 3). It was set up in accordance with a user poll, collecting data on the content and quantity of expected questions (see [ASM01], Section 3). Under the user model dimensions of [KF88] it classi es as modelling capabilities, as well as knowledge and belief.

CHAPTER 1. INTRODUCTION

20

I will rather be calling these skills and interests. In addition, some personal information is recorded (see [ASM01], Section 3 for further details). Users are modelled via an attribute hierarchy : Figures 1.6/1.73. Note that four attributes have been renamed since the original implementation (cf [ASM01], Figure 2), which aimed at modelling only skills, not interests. The structure is considered \given" in this project. One instantiation of the attribute record represents one registered Elfwood WAS user. One string value (or one of a few prede ned, xed values) may be assigned to every attribute on any level. If, for instance, a user states his or her interest is drawing \cartoons", and the assistant decides this ts neither child attribute of \Art styles", then the information is put into that parent attribute. Already stored string values can be updated and extended. Now, what about our fairy artist from Figures 1.3 { 1.5? We can model her by tting the gathered information into the following attributes. 1. Art objects (Buildings or Nature would be too speci c.) 2. 3. 4. 5. 6.

Elfwood member Name Pencil Anime/Manga Humans (In a Fantasy setting \fairies" and \elves" should be regarded human, otherwise we would end up with far too many entries in the general Art objects.) In a following consultation of the same user, the respective assistant could then call the user by her name right from the start, without having to ask another time. He or she would know that there is no artwork displayed on Elfwood to base the consultation on. Still, the assistant could start with a clear picture of the user's drawing interests in terms of preferred media, style, and objects she draws. It would be clear that the user is not skilled in creating backgrounds for drawings, and the current problem may be related to this. Note that determining what string values to insert in the attribute elds of a user model is not trivial. No assistant would place the exact dialogue phrase \I'm not a member of elfwood" in the Elfwood member eld; more likely a simpler and more precise \No" { assuming there is no previous entry, which 3 \FARP" stands for \Fantasy Art Resource Project", a compilation of tutorial articles

on creating artwork and writing ction; \FantasyHoo" is Elfwood's WWW search engine.

1.2. APPLICATION DOMAIN



Personal data Age Gender Country Occupation Name Conversation style Elfwood data Elfwood member Link to art Link to stories 

21



Art 

























Art media { Wet Ink Oil paint Watercolour Acrylics { Dry Pencil Coloured pencil Charcoal Conte Pastel { Digital Adobe Photoshop MetaCreations Painter Paintshop Pro Graphics tablets 3D programs Art objects { Humans { Animals { Buildings { Nature Art styles { Realism { Anime/Manga { Impressionist { Art nouveau Art techniques { Perspective { Sketching { Detail drawing































Figure 1.6: Elfwood user model attributes (1)

CHAPTER 1. INTRODUCTION

22



Writing Writing styles { Humour { Serious writing { Fantasy { Sci-Fi { Horror Writing technical { Grammar { Characters { Setting { Plot { Point of view





Elfwood usage Site navigation { Pictures { Stories Member functions { Intranet { Tour creation { Picture upload { FARP (creation) User functions { Text search { Attribute search { FARP (usage) { FantasyHoo Computer Internet Scanners MS Windows Linux Unix 









    

Figure 1.7: Elfwood user model attributes (2)

1.3. TOWARDS A SOLUTION

23

would further complicate matters. Considering this problem, though, reaches beyond the goals of highlighting user model information and identifying the associated attribute(s). My approach to these immediate goals is sketched in the next section.

1.3 Towards a Solution Previous work Substantial amounts of data, suitable for learning and

testing extraction rules, had been collected during the eld study [ASM01]. Altogether, a corpus of 175 consultation dialogues had been recorded, in which 30 dierent assistants consult 129 dierent users. As part of the eld study, those dialogue logs had already been manually inspected. Table 1.1 shows the results. On average, a consultation dialogue contained 1.51 user model statements.

Circumstance Total no. occurrences Average per dialogue

Q-A Vol Vol-Back Rej-Soln All circumstances

134 (50.8%) 67 (25.4%) 51 (19.3%) 12 (4.5%) 264

0.77 0.38 0.29 0.07 1.51

Table 1.1: Previous dialogue analysis statistics [ASM01] To get a clearer picture of the conversational circumstances users reveal information about themselves in, each utterance had been classi ed according to categories established by Elzer, Chu-Carroll, and Carberry in [ECCC94]. The conversational circumstances were:

 Question-and-Answer (Q-A): The user is asked for a personal detail and provides the information.

 Volunteered (Vol): The user just talks about her- or himself.  Volunteered-Background (Vol-Back): The user talks about her- or himself to illustrate the problem.

 Reject-Solution (Rej-Soln): The user turns down an advice giving personal details as a reason.

24

CHAPTER 1. INTRODUCTION

The distribution of statements regarding conversational circumstances is included in Table 1.1 as well. 50% of all discovered user information were directly asked for by the assistants. Looking at the sample dialogue from the previous section, only user model item 1 would fall into Vol-Back; all others are direct answers to the assistant's questions (Q-A). Those observations are considered, when formulating requirements on the IE solution.

Thesis idea Several approaches to and implementations of automatic user

model generation from dialogues have been introduced, among others by Kass in [Kas91]. However, those examine human-computer dialogues in natural language dialogue systems, attempting to grasp and/or actively support the user's strategy of problem solving. Dialogues of that kind are systemcontrolled, synchronous (without delay) and basically free of small talk. The approaches can aim at natural language understanding of full grammatical structures to record complex thoughts and actions. Such dialogue systems might build comprehensive syntax trees covering every word of an uttered sentence. They might transform whole user statements into a logical representation of their carried semantic information. For the user modelling, this could be the same sort of representation already used in the automatic generation of dialogue responses (see Section 3.2.1 of [Kas91]). In our application, we have human-human dialogues via a free chat. Dialogues of such kind tend to be asynchronous, discursive and grammatically incorrect. We seek to extract only skills and personal interests. Both our encountered language and our aspired goals, ie our sought-for user model information, were much more vague in nature than for the existing approaches. Consequently, full natural language understanding is not feasible. But neither is it necessary! Instead of following the above approaches, I applied information extraction techniques to the discovering of user model information in consultation dialogues. IE, as a particular natural language processing (NLP) task, understands natural language texts only partially, but with the clear intention of spotting and storing speci c information. (IE is not information retrieval, which automatically nds sought-for documents, instead of information in them. IE does less than knowledge extraction, which additionally infers rules from the extracted information. IE is dierent from data mining, which works on structured data, not natural language text.) I had found one approach that does utilise IE for extracting problem solving strategies from transcribed user-user dialogues: [Rag99], Chapter 8. Its idea was considered along with the more general methods.

1.3. TOWARDS A SOLUTION

25

Thesis outline The solution was carried out by thoroughly specifying the

problem, before implementation steps were taken. I regarded this essential in mastering the anything but trivial information extraction task.  Chapter 2 consists of the in-depth requirements analysis carried out on the previously collected consultation dialogue logs.  Chapter 3 assesses existing IE approaches, describing those two that appeared to (equally) suit the requirements and thus quali ed for being implemented.  One of the approaches is being designed and implemented using Java during Chapter 4.  The evaluation in Chapter 5 applies the system and reveals strengths and weaknesses of this particular IE implementation.  Chapter 6 recapitulates the results of the project and draws conclusions. It outlines following steps of work and suggests future research.

Contributions The Master's thesis contributes to both the elds of auto-

matic user model acquisition and information extraction. It shows how to (semi-) automatically acquire comprehensive user model information from written user utterances, without fully understanding natural language. The human modeller's workload is reduced to an assessment task nalising the text processing. The thesis examines IE techniques from a wide range of directions { neither excluding shallow keyword based approaches, nor very recent statistical solutions. It is among the rst attempts to perform IE on human-human text chat dialogues. Empirical evaluation results point out strengths and weaknesses of the chosen method, and show its eectiveness in principle. Theoretical contributions lie in the formal examination of chat language and consultation discourse. Elzer, Chu-Carroll, and Carberry's concept of conversational circumstances is reinterpreted, expanded in a new context, and successfully introduced into partial parsing information extraction. Practical contributions demonstrate the utilisation of parser generators and HTML for conveniently solving a real world IE problem. We see that \real time" IE can be desirable and feasible.

Chapter 2 Information in Consultation Dialogues Previously collected consultation dialogue logs undergo an in-depth examination, so that detailed requirements on the information extraction task can be formulated.

2.1 Dialogue Style

Terminology Dissecting consultation dialogues, this chapter refers to different syntactical sub-units within them. A line spans the whole text after or (including that tag), until the next or begins. A sentence is a part of or at most one line between two nal punctuation marks, ie usually full stops, exclamation or question marks. There is one exception: Exclamations like \Oh !" are not regarded a separate sentence, but the start of their following sentence. This is because they can indicate a certain conversational circumstance. A phrase is a part of or at most one sentence. Phrases are the text units to be highlighted and associated with user model attributes. They consist of one or several words. Let \ " stand for \is a substring of", then the order of syntax terms reads Text



Text Text



Text Text

Text

COGroup1, . . . ,

Text Text Text

Text Text Text Text

Text



Table 4.1: Tag syntax de nitions

CHAPTER 4. IMPLEMENTING INFORMATION EXTRACTION

96

#

1

Phase output elves

black they

the orks and the trees a dark colour the stores drawing colour can't be done can't draw were being made have to go how they

they

yes

I like drawing anime-style fairies and elves

Table 4.2: Tag syntax examples

4.2. IMPLEMENTATION

97

Java classes The information extraction system was implemented in Java.

One could argue that a functional or a logic programming language were more suitable, as regular patterns could be implemented more intuitively and directly. But we wanted a system which is easily integrated into the existing Java-based WAS implementation. A powerful tool to automate some of the implementing is a so-called parser generator. Usually, parser generators are being applied to compiler construction problems, but why not use them for IE as well? For C, there are \Lex" and \Yacc", and programming in Java one can employ JavaCC. A parser generator reads a regular or context-free grammar de nition, and outputs a full parser module or class. In theory, all there was to do was compile the four designed grammars with JavaCC, and we would gain the parser code for the classes listed in the diagram (Figure 4.3). In practice, JavaCC did prove to be a powerful tool, although it was hardly easy to handle. Except from technical diculties in processing large grammar les { which were solved by rewriting the expressions in another, equivalent form, and by splitting the domain events grammar in two { there are a number of subtleties in its dealing with ambiguities and lookaheads, that needed to be understood rst. For instance, once a string is de ned as a terminal, say cat, its substrings can never be parsed anymore as a sequence, in that case c a t matched against a pattern Character Character Character. Ignoring, ie not tagging, any sequence of characters was often required and being treated by allowing any de ned terminal symbol to act as a Character, if it was not in the production anyway. Parsing tags had to be done especially carefully, as they contain character sequences of inde nite lengths (the semantic information, the CO groups, the tagged text). They needed to be dissected terminal by terminal, and every kind of sequence would need to be dealt with. If one character is never in a choice con ict with a sequence of more than one other terminal, nite-state parsing still works. Implementing this in phases 3 and 4 proved to be tedious, but nevertheless feasible. In fact, the nal domain events phase, which extensively processes HTML tags, ended up with by far the lowest lookahead of all (2). It is no trivial step from a theoretically designed regular grammar to one that is really working as intended by the designer. Finally, huge grammar les require large amounts of memory to be compiled. The complex generated parsers require even more memory, when run on longer documents as a whole, and not just single lines. While JavaCC automates the implementation of parsers, it is further possible to already insert usual Java code into the grammar de nitions. These so-called actions can be attached to any regular sub-expression and are executed, whenever it is reached. The action concept was intensively exploited for inserting tags around matched expressions in sentence texts, or in special

CHAPTER 4. IMPLEMENTING INFORMATION EXTRACTION

98

cases for determining, if matched text should be tagged at all. This means mostly verifying conditions for attribute associations in the domain events phase. But they also, with looking ahead one terminal, helped decide that for instance in basic phrases i followed by m, ll, or ve is a personal pronoun (\I'm", \I'll", \I've"); but followed by a dierent letter it is not. Actions allowed inserting workarounds for a few parsing problems not solved well by a nite-state acceptor. Recall the inde nite left recursion problem: E-mail addresses had been formulated as the production 4.2. The problem was that an e-mail address cannot be recognised until the \@". E-mail addresses were therefore de ned by a right-linear production that is not pre x ambiguous: PersonaldataClass ,! \@" (Character)+

(4.3)

The attached parser action would then collect non-whitespace characters preceding the \@", ie from right to left, in the direction opposite to the parsing, and place the tag in front of them. Still, this would not include already tagged pre xes. Think of a [email protected], which includes a name. But that could later be made one complex phrase, as explained in Algorithm 4.3. JavaCC was considered useful not only for all partial parsing phases except CO, it was applied to other text processing problems as well: Sentence segmentation (the SentenceSegmenter class) and reading log les for translation into the internal language representation (LogReader in Section 5.1). All other classes were implemented in plain Java. Translating the UML diagram9 involved conceiving Java equivalents of the dierent UML concepts. As both follow the OO paradigm, most translations are trivial. However, association relationships were treated in several dierent ways: Generally, they lead to a new attribute of the type of the associated class. If cardinalities larger than 1 are involved (2 participants), the attribute becomes an array of the associated class. No new attributes were created for the automatically generated JavaCC classes; the sentence to be read and modi ed was simply passed over as a parameter. As already decided at design time, the two aggregations were made double-linked queues of the aggregated objects. Attributes were translated to global public variables, free to be read and written by outside objects that access the attributed objects via relationship links. Following is the list of all public classes with their public members and method signatures.



public class ChatDialogue

9 partly achieved automatically via the \Rose" software

4.2. IMPLEMENTATION

     

99

public String topic public Line queueHead public Line queueTail public Chatter theChatter[] public ChatDialogue()

Behaviour:

Constructor. Initialises the attributes. public void setParticipants(String id1, boolean vol1, boolean back1, boolean a1, boolean rej1, boolean pres1, boolean q1, boolean soln1, boolean com1, String id2, boolean vol2, boolean back2, boolean a2, boolean rej2, boolean pres2, boolean q2, boolean soln2, boolean com2)

In:

{ ID of the rst chatter, for a WAS user to { role of the rst chatter; for a WAS user

id1 "user" vol1 com1 true, true, true, true, true, false, false, false id2 "assistant" vol2 com2 false, false, false, false, false, true, true, true

{ ID of the second chatter, for a WAS assistant to { role of the second chatter; for a WAS assistant

Behaviour:



Establishes the participants association by creating theChatter[] with two array elds. public void insertLine(String senderID, String head, String text)

In:

{ ID of the sender, in the WAS "user" or "assistant" { the head text information, in the WAS eg "user", "cat, , or "assistant" (but also technical ones like ) { the actual chat text (or technical data like 76070, a response time)

senderID head egory" "query" text

Behaviour:

Inserts a new line into the insertionQueue, created with the given attribute values. For the rst line creates a new queue. A new queue element is double-linked with the current queueTail. The rst inserted element is the queueHead. The latest inserted element becomes the new queueTail.

100

CHAPTER 4. IMPLEMENTING INFORMATION EXTRACTION



public boolean extractComplexWords()

Out:

, if segmentation and extraction were successful

true

Behaviour:



For each line in the queue calls Line.segmentIntoSentences() and then Line.extractComplexWords(). public boolean extractBasicPhrases()

Out:

, if segmentation and extraction were successful

true

Behaviour: 

For each line in the queue calls Line.segmentIntoSentences() and then Line.extractBasicPhrases(). public boolean extractComplexPhrases()

Out:

, if segmentation and extraction were successful

true

Behaviour: 

For each line in the queue calls Line.segmentIntoSentences() and then Line.extractComplexPhrases(). public boolean extractCO()

Out:

, if segmentation and extraction were successful

true

Behaviour: 

For each line in the queue calls Line.segmentIntoSentences() and then Line.extractCO(). public boolean extractDomainEvents()

Out:

, if segmentation and extraction were successful

true

Behaviour:

For each line in the queue calls Line.segmentIntoSentences() and then Line.extractDomainEvents().



public class Line

     

public String headText public String text public String extractedInformationText public Sentence queueHead public Sentence queueTail public ChatDialogue theChatDialogue

4.2. IMPLEMENTATION

     

101

public Chatter theChatter public SentenceSegmenter theSentenceSegmenter public Line successor public Line predecessor public Line()

Behaviour:

Constructor. Initialises the attributes. public boolean segmentIntoSentences()

Out:

, if the segmentation was successful

true

Behaviour: 

Has the segmentationQueue constructed by a SentenceSegmenter. Required initialisation, before any extraction can start. public boolean extractComplexWords()

Out:

, if the extraction was successful

true

Behaviour:



Performs one phase of IE on the line by calling Sentence.tag() for each sentence in the queue. Updates text and extractedInformationText, concatenating the modi ed sentence texts. public boolean extractBasicPhrases()

Out:

, if the extraction was successful

true

Behaviour:



Performs two phases of IE by two times calling Sentence.tag() for each sentence in the queue. Updates text and extractedInformationText, concatenating the modi ed sentence texts. public boolean extractComplexPhrases()

Out:

, if the extraction was successful

true

Behaviour: 

Three phases of IE by three times calling Sentence.tag() for each sentence in the queue. Updates text and extractedInformationText, concatenating the modi ed sentence texts. public boolean extractCO()

Out:

, if the extraction was successful

true

Behaviour:

102

CHAPTER 4. IMPLEMENTING INFORMATION EXTRACTION



Four phases of IE by four times calling Sentence.tag() for each sentence in the queue. Updates text and extractedInformationText, concatenating the modi ed sentence texts. public boolean extractDomainEvents()

Out:

, if the extraction was successful

true

Behaviour:

All phases of IE by ve times calling Sentence.tag() for each sentence in the queue. Updates text with a concatenation of the modi ed sentence texts. Removes all and tags from the text, and stores the result in extractedInformationText.



public class Sentence

            



public boolean answer public boolean rejection public boolean presentation public boolean question public boolean solution public boolean comment public String text public int completedPhases public Line theLine public Sentence successor public Sentence predecessor public ParserRouter theParserRouter public Sentence()

Behaviour:

Constructor. Initialises the attributes, especially the six boolean ones to false, and completedPhases to 0. Creates theParserRouter to enable information extraction. public boolean tag()

Out:

, if the tagging was successful

true

Behaviour:

4.2. IMPLEMENTATION

103

Hands the sentence over to theParserRouter to have the appropriate next tags inserted in text, and possibly component attributes set.



public class Chatter

         

public String chatID public boolean volunteered public boolean background public boolean answer public boolean rejection public boolean presentation public boolean question public boolean solution public boolean comment public Chatter()

Behaviour:

Constructor. Initialises the attributes to "chatter" and false. Otherwise, this class only stores data.



public class SentenceSegmenter



public boolean segment(Line oldLine)

In:

oldLine

Out:

{ a chatter's line with an empty queue

, if the segmentation was successful

true

Behaviour:

Constructs a queue of new Sentence objects that are the segments of oldLine. Inserts each queue element directly into the oldLine object. The design was implemented as a short JavaCC grammar, and the class was automatically generated from that. It parses whole sentence texts of characters, exclamations, abbreviations, and separators.



public class ParserRouter

 

public Sentence theSentence public ComplexWordsParser theComplexWordsParser

104

CHAPTER 4. IMPLEMENTING INFORMATION EXTRACTION

     

public BasicPhrasesParser theBasicPhrasesParser public ComplexPhrasesParser theComplexPhrasesParser public CO theCO public DomainEventsParser theDomainEventsParser public ParserRouter()

Behaviour:

Constructor. Initialises the attributes. public boolean call()

Out:

, if the parsing was successful

true

Behaviour:

Checks the completedPhases attribute of its sentence. Decides the next phase. Creates a parser object to perform that phase with theSentence.text as the input stream. Passes the whole object theSentence on to the parser, so that it may access attributes and insert tags in the text.



public class ComplexWordsParser



public boolean parse(Sentence oldSentence)

In:

oldSentence

Out:

{ untagged sentence

, if the parsing was successful (always the case, unless an internal error occurs { every sentence matches)

true

Behaviour:

Calls the parser generated from the complex words grammar by JavaCC. The parser matches the input stream against the starting production. Whenever a terminal sequence from the input stream matches with a sub-expression for which an action is de ned, the attributes of oldSentence are read and written accordingly. This phase sets the boolean component attributes.



public class BasicPhrasesParser



public boolean parse(Sentence oldSentence)

In:

oldSentence

Out:

{ sentence after complex words

, if the parsing was successful (always the case, unless an internal error occurs)

true

4.2. IMPLEMENTATION

105

Behaviour:

Calls the parser generated from the basic phrases grammar by JavaCC. The parser matches the input stream against the starting production, and executes de ned actions, ie in this phase the insertion of tags.



public class ComplexPhrasesParser



public boolean parse(Sentence oldSentence)

In:

oldSentence

Out:

{ sentence after basic phrases

, if the parsing was successful (always the case, unless an internal error occurs)

true

Behaviour:

Calls the parser generated from the complex phrases grammar by JavaCC. The parser matches the input stream against the starting production, and executes de ned actions, ie in this phase the combining of tags.



public class CO

Parameters:

In the code, constants de ning dierent scope sizes may be changed.

 



public Sentence theSentence public CO(Sentence coSentence)

Behaviour:

Constructor. Initialises theSentence with coSentence. Builds the sentence scope for searching backwards according to Algorithm 4.5, steps S1 to S3. public boolean resolve()

Out: true

(no identi ed case where running the method would fail)

Behaviour:

Executes Algorithm 4.5, steps R1 to R4 (calling a number of auxiliary private methods for dierent tasks). Reads predecessors of oldSentence and sentences from lines preceding the line of oldSentence. Reads oldSentence and updates tags in it.



public class DomainEventsParser

Parameters:

106

CHAPTER 4. IMPLEMENTING INFORMATION EXTRACTION In the code, constants de ning seven highlighting colours may be changed. Two private methods de ning the physical highlighting tags may be replaced.

Notice:

Due to memory limitations during compilation, this largest grammar le had to be split into two. The two generated parsers are executed in sequence. They comprise basically the same POS productions, but two disjoint sets of association conditions. The second part (covering the attribute trees Elfwood usage, Computer, and the attribute Extended, as well as all Pres-Com rules) parses a sentence tagged in part one, ignoring all highlighting tags.



public boolean parse(Sentence oldSentence)

In:

oldSentence

Out:

{ sentence after CO

, if the parsing was successful (always the case, unless an internal error occurs)

true

Behaviour:

Calls the parser generated from the domain events grammar by JavaCC. The parser matches the input stream against the starting production, and executes de ned actions, ie in this phase inserting attributes based on the association conditions for dierent POS structures.

Keyword based interpretation From an implementation point of view,

the most interesting dierence between partial parsing and the keyword based approach would lie in the actual low-level parsing techniques. How could keyword matching be performed eectively and eciently? Regular grammars and JavaCC would probably be less suitable for the implementation of keyword based interpretation. Finite-state parsers require space and time. Sequential structures with ambiguities between substrings bear unnecessary limitations, when a sequence is not required by the approach. Keyword based interpretation could use simpler data structures and more ecient matching or look-up algorithms. One possibility would be a balanced binary search tree to store the keywords. Words are accessed quickly and matching could be performed character by character. Navigating in the tree would allow correction of spelling mistakes by choosing an alternative branch (another letter), if a following character was not matched. The next expected character could be tried instead, as well as the characters left and right on the keyboard. The keyboard

4.3. INTEGRATION

107

layout does in uence the nature of typing mistakes { cf [Ing97], page 8. Another suitable data structure might be a hash table of keywords. Every word would have a distinctive hash value. The hash value of an encountered expression could be matched against hash values of keywords, tolerating a certain dierence. Measures for computing the distance between two words include the Levenshtein distance and the weighted Levenshtein distance { cf [Ing97], page 26.

4.3 Integration IE from dialogue logs The implemented system provides all IE function-

ality, as well as the interface to integrate it in a WAS (or any other text chat environment). Like a human Web assistant, the automation may perform extraction of user model information either on a chat log le, ie after the conversation has taken place, or while the chat conversation is evolving. To process a nished dialogue, the environment needs to collect all relevant chat data from the log le, have the IE system build its internal representation of the chat data, execute IE on the whole text, and then have the results written on the screen. Algorithm 4.6 describes the four steps in terms of the concrete implemented interface.

Algorithm 4.6 (Application to a whole dialogue) Interaction with the IE system happens via the classes

ChatDialogue

and

Line.

1. Read chat data from the internal chat system or a log le. 2. Create a ChatDialogue. Set its topic attribute, and two chat participants via setParticipants(). Fill the chat dialogue with all dialogue lines by calling insertLine() for each. 3. Execute the desired extraction method of of IE extractDomainEvents().

ChatDialogue

{ for all phases

4. Write the result { for full extraction Line.extractedInformationText of every Line { as an HTML document, and open it in a new Web browser window. (An applet may open a browser window by calling java.applet.AppletContext.showDocument().)

For a start, the testing and evaluation environment, which is described in Chapter 5, follows this simpler mode of processing \static" documents.

108

CHAPTER 4. IMPLEMENTING INFORMATION EXTRACTION

\Real time" IE The \dynamic", \real time" case means performing IE

on an evolving dialogue, while assistant and user are chatting. Firstly, the environment would need to collect basic data about the chat dialogue. It would then make the IE system build its internal representation incrementally by always adding the latest sent line. IE would be executed on those single lines, with having the results so far written on the screen, whenever requested. Algorithm 4.7 describes the steps in terms of the concrete implemented interface.

Algorithm 4.7 (Application to line after line) Interaction with the IE system happens via the classes

ChatDialogue

and

Line.

1. Read chat data from the internal chat system. 2. Create a ChatDialogue. Set its topic attribute, and two chat participants via setParticipants(). Insert the rst dialogue line via insertLine(). 3. Execute the desired extraction method of the latest inserted all phases of IE extractDomainEvents().

Line

{ for

4. If no HTML document for the dialogue exists, create one. Append the result { for full extraction Line.extractedInformationText { to the HTML document. If there is a current request for viewing the information, open it in a new or already existing Web browser window. (An applet may open a browser window by calling java.applet.AppletContext.showDocument().) 5. Add a newly read line via ChatDialogue.insertLine(), and continue with 3.

Algorithm 4.7 ensures that the costly IE algorithm may run concurrently with the actual chat. Also, the hypertext document displayed by the browser will be updated only on request by in our case the Web assistant. Notice that writing the HTML le must happen on the server side, possibly integrated in the logging procedures. An applet can normally not write les to the client hard disk, and for convenience and security of the user it should not either.

Chapter 5 Results The implementation of partial parsing is being applied to the collected consultation dialogues, and evaluated for its performance.

5.1 Application Testing log les Instead of an immediate integration into the existing

WAS implementation, only a rst testing environment was created. It allowed intensive testing while programming, which resulted in iterative design improvements and optimising the implementation. The testing environment was reused during the evaluation of system performance (in Section 5.2). Figure 5.1 shows the user interface to the testing environment: The tester may enter the input lename of an existing log le to be processed and the output lename of the HTML le containing the tagged log text. Extraction may be performed up to any of the ve extraction phases, in order to gain intermediate results. Alternatively, the nal version, containing no more or tags, may be requested. Pressing the \Tag" button starts the processing on the le. When the applet reports it has nished, or that an error occurred, the tester can load the created HTML le in a Web browser and study the results. The testing applet was not run in a browser itself, but in the Java \Applet Viewer", which does allow saving les on the hard disk. The option \Evaluation" may be selected to set the system in evaluation mode (Section 5.2).

Implementation Figure 5.2 displays three new UML classes with their link to the core IE system (described in the previous chapter): The new 109

110

CHAPTER 5. RESULTS

Figure 5.1: The testing applet

Figure 5.2: UML class diagram: Application

5.1. APPLICATION

111

class TestingEnvironment implements the applet. It provides the user interface and executes Algorithm 4.6 for whatever input. The class interacts with the core IE system by creating specialised ChatDialogue objects: The sub-class LoggedWASDialogue inherits all members from ChatDialogue and extends the class by adding two more methods for reading a dialogue (Algorithm 4.6, step 1) and writing it (step 4). readLog() makes an auxialiary class translate les from the WAS chat log format into the internal system representation. It sets the topic, participants, and calls insertLine() for each line in the dialogue le (step 2). It is implemented, again, as a short JavaCC grammar. After any \run" of the system (step 3), initiated by one of the TestingEnvironment methods, writeHTML() creates the hypertext document of the processed dialogue. It inserts the line texts of all Line objects the LoggedWASDialogue aggregates. (As a sub-class, LoggedWASDialogue is a ChatDialogue in Figure 4.2.) The classes were implemented and the applet applied to many of the collected log dialogue les. While small dialogues were parsed rather quickly and with no need for extra memory, the longest le took several minutes of processing time and used up 140 MB of RAM. The memory issue may need to be investigated further, especially if problems in \real time" application occur. However, the \real time" implementation would be based on Algorithm 4.7, which works on single lines only. Memory may not be a worry at all; processing time is still much shorter than typing time.

Application results Figure 5.3 is a screenshot excerpt from a processed

dialogue. The HTML output of the system has been loaded into a browser; highlighted phrases are displayed, as well as attribute tool tips, whenever the mouse cursor is over a highlighted phrase. Viewed by an assistant, the displayed extracted information teaches her or him that the assisted user owns \Paintshop Pro" versions 3 and 6, and that he does not have \Adobe Photoshop". Inserted into the user model, this information will never need to be asked again, when consulting the person in matters of digital art manipulation { except, of course, to later check, if the user has acquired the Adobe program in the meantime. The example demonstrates how in processing a dialogue everything works exactly as intended. The system extracts the same information as the manual analysis. There were in fact many cases where it worked that well, and there were many others where it did not. Any nal success relies on good intermediate results. Table 5.1 lists the intermediate and nal extraction results for all parsers in the case of the screenshot example (see also the previous Figures 4.5 and 4.6).

112

CHAPTER 5. RESULTS

Figure 5.3: Displayed extracted information A few details are noteworthy: Both lines were segmented into one sentence, ie not segmented all. This is correct even in the rst line, because No... is not followed by a space charatcer. The three dots act merely as a comma, connecting the words, rather than separating them. That sentence can act both as an answer and a rejection. It is the answer component that in the CO phase leads to a successful resolution of No, and in domain events to an association with the right user model concept, obtained from a question above. Searching backwards for matching conversational circumstance components proved to work very well and in a number of cases helped avoid a wrong resolution. version is resolved correctly, because of its carried CO group. The decision to look for the rightmost match in the lines above proved to be very important. Whether opting for the leftmost expression within the same sentence (a leading concept) implies better results than taking the righmost one every time { which would have made the scope a perfect stack { was not so clear. The connection of 3 and 6 to one NumberVerbNounGroup in complex phrases makes it match with the same pattern in domain events that would have matched with a single number. The information that the user owns two

5.1. APPLICATION

Parser LogReader

21:26:6

Complex Words Parser

Basic Phrases Parser

Complex Phrases Parser CO

Domain Events Parser

21:33:5

Line:

Line:

Sentence:

Sentence:

No...but I have paint shop, which is close, but not quite. Sentence Segmenter

113

No...but I have paint shop, which is close, but not quite. No but paint shop

) answer ) rejection

{ PaintshopproClass: Computer, Singular No { Reference: Question have paint shop { PaintshopproVerbNounGroup: Computer, Singular is { beVerbGroup close { Adjective but { Conjunction I { FirstPersonPersonalPronoun which { RelativePronoun { Reference to: AdobephotoshopVerbNounGroup No { Adobe Photoshop I have paint shop { PaintshopPro No

I have version 3 and 6.

I have version 3 and 6.

{ NumberClass { NumberClass { Reference: Computer

3 6 version

{ NumberVerbNounGroup { NumberVerbNounGroup { haveVerbGroup { Conjunction { FirstPersonPersonalPronoun

3 6 have and I

{ NumberVerbNounGroup

3 and 6

{ Reference to: PaintshopproVerbNounGroup have version { PaintshopPro

version

I

I have version 3 and 6

PaintshopPro

Table 5.1: Sample extraction results

{

114

CHAPTER 5. RESULTS

dierent versions of the program is not lost. Observe that already I have leads to an association with Paintshop Pro. The tagging is applied, but the parsing procedes. It nds a larger structure including the sux to also be user model information. It therefore tags the larger phrase, but only from the rst new word onwards. 3 and 6 encountered alone would not have been tagged.

version

5.2 Evaluation

Method The performance of the partial parsing implementation was to

be evaluated empirically. Evaluation data was collected by applying the system as described in Section 5.1 to the testing corpus of 175 dialogues. A common evaluation method, especially in machine learning, is to divide collected domain data into two sets of testing and training data. System rules or parameters are learned from the training data, independently of the testing data. Afterwards, the system is evaluated based on the testing data, independently of the training data. Our training/testing set of user model phrases in consultation dialogues was pretty small, especially considering the number of user model items in relation to the number of target attributes. I assumed that every bit of potential training data available was necessary in building a system of some quality. I therefore decided to use the same dialogue corpus in \training"1 and testing. To nevertheless guarantee independence of an evaluated dialogue from knowledge acquired during its analysis, I relied on an all but one strategy in the evaluation. Parallel to design and implementation, such basic phrases productions and attribute associations in domain events had been marked that had been derived from one dialogue source only. Remember that the rules speci cation in Chapter 2 kept track of the dialogue sources. In now conducting the evaluation on the \training" dialogue corpus, a production or association would be disabled, if testing was performed on the very dialogue it had been derived from. This ensures the independence of the testing material { one dialogue at a time { from the \training" material { all other 174 dialogues. Comparing the all but one evaluation to an additional unrestricted run of the system, from now on referred to as test on training, would further show exactly how dependent the system is on its \training" data. Technically, the disabling of parts of the system was achieved by adding a static variable (one shared by all objects) to the ParserRouter class: Figure 5.4. evaluationFile stores the name of the log le currently being eval1 The term \training" makes more sense in a machine learning context.

5.2. EVALUATION

115

uated. It is set by TestingEnvironment, if the tester activated the \Evaluation" option in the testing applet (Figure 5.1). The code for disabling productions and associations in complex phrases and domain events actions reads ParserRouter.evaluationFile to determine the current log lename. Whenever a production or association bearing the lename matches, it is either ignored (in domain events), or it returns a neutral, ie untagged result (in basic phrases).

Figure 5.4: UML class diagram: Evaluation

Limitations Complex phrases and CO need no special treatment in an

evaluation, as both were designed independently of concrete dialogue logs. But what about the complex words grammar? It was derived mostly from the vocabulary list, whose entries where not marked with their sources. This had been mainly in the assumption that these were too many, and entity classes would be largely lled with compiled words from vocabulary sources. Moreover, it was understood the NE phase is domain dependent anyway. No IE system would work well without this initial \training" data. It might not have been of primary interest to measure performance without compiled NE vocabulary. Recapulating those decisions now, it must at least be stated that they do pose limitations on the empirical results. Performance is measured independently of grammar rules derived from dialogues, but not independently of collected named entities. Only an application to dierent dialogues from the same domain can show, if the created complex words grammar sustains, or if it requires further

CHAPTER 5. RESULTS

116

expansion. [CV01] suggests a machine learning algorithm for automatically improving the performance of a given NE system, or for moving it to a slightly dierent domain. It learns from the already tagged named entities and their syntactic and semantic context. In their implementation, the authors rely on WordNet as well, when they generalise vocabulary. Another problem posed by the speci cation lies in the rules. They de ne comprehensively what kind of phrases bear what user model information. They do not specify, however, what kind of phrases do not contain user model information. i have no background should not eect an Art objects association. i have a cat does not relate to Animals, neither does my fantasy work to Occupation. To exclude such unwanted associations, exceptions in domain events conditions were formulated. For instance, an association with Occupation should not happen, if the noun group or verb/noun in question includes the semantics of Fantasy (or Sci-Fi etc). Associations with Humans based on the Humans semantics without an artwork context were restricted to not happen in a Stories or Writing context. This prevents eg fairy stories to be associated with Humans. Conditions might have been formulated as requiring only the semantic elements speci ed. But the output of complex phrases are connected words that contain semantics of several entity names and POS. They had to be treated from a more general point of view, allowing irrelevant elements in at least the semantics set of the complex word. Exceptions on conditions were gained from testing the system on the available dialogues. That created a new dependence between testing and \training" material. Normally, making out where conditions \overgenerate" and then modifying the system should be an iterative process on training data ([AI99], page 7). Marking the sources of all exceptions, so that they could have been included in the all but one strategy appeared to be too tedious work though. In order to not further limit the value of the evaluation results, an additional run of the system was conducted after disabling all exceptions in the code. This will be referred to as the no dependence run. The actual performance discrepancy between all but one with exceptions and all but one with no dependence would indicate the eective impact of such exceptions.

Measure The common evaluation criteria for the performance of IE sys-

tems are recall and precision. Recall is the percentage of how much of the wanted information (in our case that speci ed during Chapter 2) is extracted. Precision is the percentage of how much of the actual extracted information is correct. The de nitions are (after [SFL97]): Recall =

TP TP + FN

5.2. EVALUATION

117 Precision = TPTP + FP

TP: true positives, ie the number of correctly extracted pieces of information FN: false negatives, ie the number of mistakenly missed pieces of information FP: false positives, ie the number of mistakenly extracted pieces of noninformation

The motivation guiding the decision about exactly what pieces should be considered correctly extracted, missed, or mistakenly extracted information was to determine their actual value to a Web assistant. Presumably, an assistant would be able to insert information into the user model appropriately (and without much extra reading or thinking), if she or he was pointed to the whole sentence and some attributes, or to parts of the sentence and all attributes. During the second manual dialogue analysis in Chapter 2, user model phrases were underlined2 and attribute associations noted in all 175 collected dialogues. These form the set of correct information pieces. The IE system occasionally did nd additional information pieces that appeared to be correct. Those were disregarded in measuring the performance, ie they were neither counted as true positives, nor as false positives. Extracted information that was incomplete, ie the highlighted phrase was too short, and too few or too general attributes were associated was counted as a false negative, ie \missed" information. One true positive is therefore every highlighted phrase or sequence of highlighted phrases read together, which

 either covers most words of an underlined phrase and is associated with some of the attributes noted,

 or one that covers most words of an underlined phrase and is associated with all, but a little more general attributes in the hierarchy,

 or one that covers some words of an underlined phrase and is associated with all attributes noted.

A few additional words in the highlighted phrase are allowed, as well as additional attributes as long as they do not lead to wrong assumptions. 2 The verb \underline" refers to the work of a human analyst. The verb \highlight"

refers to the system output.

118

CHAPTER 5. RESULTS

One false negative is any phrase underlined in the manual analysis, for which no corresponding true positive can be made out in the processed dialogue. One false positive is any highlighted phrase or sequence of highlighted phrases to be read together, which is not a true positive, and judged by intuition implies some wrong user model information or no, not even incomplete, user model information. The quantities of true positives, false negatives, and false positives resulted from applying Algorithm 5.1 to the system output after all ve phases for each of the dialogues. Algorithm 5.1 (Evaluation) Compare system output and manual analysis for the dialogue. 1. Count every phrase or phrase sequence in the dialogue that can be matched with an underlined phrase as a true positive as one occurrence of a true positive. 2. Count every underlined phrase not matched in 1 as one occurrence of a false negative. 3. Judge every highlighted phrase or phrase sequence not matched in 1. If it contains wrong or no user model information, count it as one occurrence of a false positive.

Evaluation results Table 5.2 gives the results gained from applying Al-

gorithm 5.1 and computing the percentages.3 Notice the three dierent evaluation strategies. The third column, no dependence, is the actual, relevant one, displaying testing results collected and calculated independently of any \training" data, except named entities. The rst two evaluation runs were conducted for comparison with the third, and to get an idea of what an \ideal" implementation would be capable of.4 3 Only 173 dialogues are included in the statistics. The remaining two could not be

processed completely by the system, because of occurrences of actual WAS tags () in the chat text, which caused the LogReader to malfunction. Counting extraction results of the partially processed dialogues in reduces recall to 83.5%, 53.3%, 52.8%, and precision to 31.6%, 22.9%, 21.9%. 4 The three manual countings were done in parallel, in order to ensure that exactly the same underlined user model phrases would be checked with the respective highlighted information: For the 173 dialogue logs, 507 of such checks were made with each evaluation strategy; 525 checks for all 175 dialogues. Still, the manual counting was somewhat inaccurate, recalling that a total of 556 user model phrase occurrences had resulted from the speci cation analysis.

5.2. EVALUATION

119

Variable Test on training All but one No dependence TP FN FP Recall Precision

430 77 918 84.8% 31.9%

277 230 920 54.6% 23.1%

274 233 968 54.0% 22.1%

Table 5.2: Measured system performance

Discussion The value of 84.8% recall for test on training shows the imple-

mentation was successful in translating most of the speci ed grammar rules into a running IE system. The system will do its job not only in theory and on the dialogues collected. Applied to new, previously unknown WAS dialogues, it will discover and correctly associate more than every second user model item. The 54% recall for no dependence (and all but one) suggests it works that general. Comparing test on training recall with no dependence recall, the system actually extracts a lot more than every second \possible" item. Between all but one and no dependence there is only a slight dierence in percentages. Exceptions on association conditions improved precision by 1%. To achieve more precise results, specialisation and re nements would need to take place on lower levels already, when tagging complex words and basic phrases semantically. Recall of user model information appears to be high, considering the dif culty of the particular task. As Chapter 2 pointed out, text chat dialogues contain more coreferences and grammatical irregularities than standard documents. No interannotator agreement (see [AI99], page 5) was measured to determine the diculty for a human being. Yet, Chapter 2 did already reveal substantial disagreements between two analysts reading the log les. The good 54% recall is due to a good generalisation over concrete words, especially verbs. CO works well, when pairing answers with questions and rejections with solutions. In a number of cases, information additional to the manually underlined was found. Situations like do you have a scanner? [...] yes

are reliably recognised. This works already on the basis of recognising conversational circumstance components via keywords, here do you and yes. Those resolution decisions based only on CO groups worked well in fewer

CHAPTER 5. RESULTS

120

cases, though de nitely better than they would have without such groups. Unfortunately, there was no obvious pattern of situations in which looking for the leftmost or for the rightmost consistent complex phrase turns out more successful { ie no direct support for the design decision in step R3 of Algorithm 4.5 (originally Algorithm 3.1). Results for precision appear to be poor, considering the intention of accurately estimating user skills and interests for consultation purposes. Remember though, that we were not grasping at the precise database output of traditional, easier IE tasks processing correctly spelt monologues. A comparison with previous implementations and their performances is dicult. Precision is where the strengths outlined above, resulting in good recall, turn into weaknesses. The system works in such a general manner that it extracts many unwanted phrases on its way of correctly identifying desired user model statements. To tag one-word expressions or one-word references, in addition to the complex POS structures, is sensible when looking for answers to user model questions. But to tag every Name or every Humans expression mentioned by the user leads to many more wrong identi cations and associations than correct ones. It had been a design decision to capture as much correct information as possible { in evaluation terms: to aim for a high recall rst (sometimes close to the atomic approach described in [AI99], pages 31 and 32). Only quite general semantic requirements were put on verb groups during domain events. Three domain independent semantic concepts were applied in basic phrases { Personal, Skill, and Interest { but these seem not enough. Verbs like \use" or \ nd", which were tagged only as a VerbGroup, proved to be particularly misleading. They should at least have been identi ed with their own semantics, like a \Usage" in that case. Further mistakes were not to exclude phrases following \how do I . . . " or \where can I . . . " (and the like): How do I draw dragons?

Coreferring terms were recognised as determiners in complex phrases and connected to a following noun. However, no production was de ned for further connecting such constructs with following nouns. This resulted in wrong associations like I already have one vampire story

with the Art attribute Humans, and not a Writing attribute. There are only a few pieces of user model information in a consultation dialogue, and the motivation had been to show the assistant as many of them as possible. For this purpose, the ratio of high recall { low precision

5.2. EVALUATION

121

is acceptable. Scanning a dialogue text for information to be entered into a user model will still require some manual selection work, but with a largely reduced eort for the assistant. To that degree, the project was successful. Workload of the human assistants is reduced; still they remain there to adjust imprecisenesses of the automation. The ratio is not acceptable in a further, eventually full automation of the user model acquisition task. The automated insertion of user information into the model, which cannot rely on human intuition and insight, would require more precisely tagged dialogues as its input. It would be necessary to identify the most reliable POS patterns and association conditions, and to switch o the rest. A start might be only the FirstPersonPersonalPronoun VerbGroup NounGroup pattern and Yes/No answers with very restricted conditions. While too simple productions generalised too much, actions for complex productions had to divide or truncate tags for fully recognised phrases. This was in order to prevent multiple tagging of the same phrase, when its pre x could already be recognised. This disadvantage of a sequence oriented parser would further complicate subsequent processing of tagged phrases. A fully automated user model acquisition system should not conclude that the user is a (skilled) artist, when receiving I'm no great shakes as an artist

(associated with Art) as its input. Either phrases like this are ltered out beforehand, or the system would in some way have to consider the context. It might be that a keyword based IE system would have produced better tags than the implemented sequence oriented partial parser. However, a keyword based system cannot catch all pronouns, adjectives etc in a tag. The observation that one-word associations were often wrong is clearly not in favour of a keyword technique. On the other hand, if CO with conversational circumstance components recognised on the basis of keywords already worked ne, then the keyword based interpretation approach should oer some freedom to include precision improving measures.

Chapter 6 Conclusions Overall results are recapitulated, and potential future research is suggested.

6.1 Recapitulation

Solving the problem The problem given was to solve a task of automatic

user model acquisition by applying information extraction methods. The automation was expected to facilitate the work process of Web assisting. A set of 175 text chat dialogues obtained from an earlier study about Web assistant systems provided the data which the development of the IE prototype would be based on. The application domain was a Web site about Fantasy art and literature. A user model structure covering skills and interests in that eld had already been established. The problem solving process started with a detailed examination of the collected data, and it led to insights about the psychological and linguistic nature of text chat and consultation dialogues. The grammatical structure of statements containing user model information was speci ed. A subsequent literature study revealed two IE approaches that could be adapted to solve this particular extraction problem. One of them { a partial parsing strategy { was implemented in the Java programming language. Grammar les were needed to execute the parsing algorithms on. These were derived from the aforementioned speci cation. The collected dialogues thus acted as \training" data for the manual creation of the system. They were further used for testing and empirically evaluating performance of the nished system in terms of recall and precision. An all but one method assured independence of the testing set from the \training" set. Recall turned out to be rather high, and precision pretty poor. A qualitative analysis of the extracted information 122

6.1. RECAPITULATION

123

{ which may be displayed in a Web browser { showed us why. The ndings give information about how and how far user model aquisition from text chat dialogues can bene t from information extraction techniques. The other way around, they present ways of enhancing IE to successfully work on the text form of dialogues.

User model acquisition results The thesis examined ways of automat-

ically generating models of user skills and interests in the context of a Web assistant system. Our motivation was to support assistants in their counselling job. The method was to (1) highlight user model information in the text chat consultation dialogues between users and assistants, and (2) associate these phrases with semantic user model concepts. An IE system automating the task was conceived and successfully realised. Without aiming at full understanding of natural chat language, it is able to identify more than every second piece of user model information, at the time user and assistant are chatting. Along the way, an amount of irrelevant and incorrect information is being extracted. The Web assistant needs to sort out unwanted pieces, and then insert the correct information into the model. The project did succeed in reducing the user model acquisition task to a now semi-automatic process. This should be much more convenient for the Web assistants and make their job more ecient. A full automation of the user modelling process on the basis of the results achieved promises to be possible, but challenging.

Information extraction results During thesis work, ways of information extraction that might have suited our problem were examined. A keyword based approach and a partial parsing approach were chosen, and the latter was implemented. Partial parsing as suggested by the FASTUS authors Hobbs et al was expanded to t the particular requirements posed by text chat dialogues. Conversational circumstances, as introduced by Elzer et al, were re ned to a concept of pairs of conversational circumstance components. These were successfully integrated into the FASTUS approach. An existing algorithm for anaphoric coreference resolution was updated, so that it now grasps conversational references made by one dialogue participant to previous utterances of another dialogue participant. The resulting model of dialogue partial parsing was put into practice in the form of a Java system running on parsers automatically generated from regular grammar de nitions via JavaCC. Its output are logical and physical HTML tags inserted directly into the dialogue text.

124

CHAPTER 6. CONCLUSIONS

The concrete system achieves apparently good recall and apparently bad precision; a ratio resulting from design decisions based on requirements assumptions. Although not yet tested, the IE should work in \real time" and on any type of text chat dialogue, as long as user model information is sought for. A move between domains will always be feasible, as only some, isolated parts { one and a half regular grammar les { need to be updated. It would further be interesting to try the system on preprocessed spoken dialogues. If IE can bene t from speech recognition research (cf the HMM approaches in Chapter 3), then why not vice versa?

6.2 Future Directions Further work The next step would be to integrate the implemented IE

system into the existing WAS. Section 4.3 (Algorithm 4.7) describes in detail how this is achieved. The updated WAS implementation could then be applied to the Elfwood WIS in another eld study, evaluating the practical use of semi-automatic user model acquisition. Given a success of the real world experiment, steps towards full automation may be undertaken. Assistants might be aordable or available on a limited basis only (like in Elfwood), and they should be able to concentrate primarily on the counselling job. A system that inserts tagged user model information into attributes by itself can work directly on the (domain events) HTML tags output. Anyhow, restrictions increasing precision of the partial parsing system should rst be set (as suggested in Section 5.2). The resulting output should be further ltered by the new system. It would then have to decide for each reliable information phrase what values to store in the user model. To assure the adequateness of the models, every insertion to take eect should still require an acknowledgement by a human Web assistant. Sensibly, the fully automated extraction system would work with more re ned attribute data types than free strings. Re ning data types might inspire a parallel re nement of NE entries and patterns (complex words). Designing the automatic insertion of user model values based on existing entries is expected to involve the IE technique of merging information (see eg [AI99], pages 32{36) as well as further sub-tasks of TE, TR, and ST. The general question remains: Is FASTUS partial parsing the most suitable approach for the purpose of dialogue IE in WAS user modelling? There are several options for alternatives, to which the performance of this rst prototype may be compared. Keyword based interpretation appears to be promising, and Section 3.3 already outlines how the idea may be applied to the problem. Automatic training approaches of partial parsing are being

6.2. FUTURE DIRECTIONS

125

investigated, and in the long run those might provide more convenience for WIS and WAS designers, at only slight costs regarding performance. If the knowledge engineered partial parsing solution persists, then ways must be found to facilitate manual grammar creation. Hand-crafting JavaCC productions was tedious work. Hobbs et al have developed \FastSpec", a higher level speci cation language (as mentioned in [HAB+ 96]).

Improving performance Whatever the alternatives might achieve, the

project results show that partial parsing already leads to average results with the implementation of a rst prototype. We can learn from weaknesses and improve system performance by undertaking a number of steps adumbrated in the previous chapters. Summarised in order of processing phases, these were:  Inclusion of a separate correction of spelling mistakes  Re nement of semantic NE and POS tagging (complex words and basic phrases)  Re nement of CO groups  Re nement of TE, in particular the association exceptions (in domain events)  A more formal integration of the user model structure, or even an abstraction from it, having it passed over to the TE grammar (domain events) as a parameter  Aecting all phases: Correction of unsuccessful pieces in the design, according to lessons learnt in Section 5.2 Improving TE (domain events) appears to be of major importance. At the same time, improving that nal phase is particularly dicult. Firstly, its quality depends on reliable \training" data. But the data collected is sparse in user model statements and grammatically incomplete or irregular. Secondly, the representation of TE as a grammar of POS tags plus association conditions is quite complicated. The problem of sparse training data might be dealt with by updating TE patterns while the system is being applied in practice. The more consultation dialogues are recorded, the more re ned the patterns can become. At least theoretically, when disregarding the diculty of complicated representation, the gradual improvement of TE patterns could be done automatically { by automatic training. Its machine learning would be able to start with an

CHAPTER 6. CONCLUSIONS

126

established set of grammar patterns, and in the long run would have been fed with a large training corpus. The pattern generalisation approach mentioned in Section 3.5 suggests the integration of automatic training into the natural work ow of the system user ([AS01a], Section 3, the Web assistant example). Technically, it performs generalisation { and further specialisation { of TE patterns in terms of ontologies. Such ontologies are basically hierarchies of \is a" relationships between concepts (like [BS00], Figure 1). Already carried out in the WAS context, albeit on a dierent problem and as a standalone system, the idea should t the user model acquisition task well. The work ow integration could happen in the fully automated system, at the point where an assistant acknowledges a user model insertion (see Further work above). The IE system would have identi ed a user model phrase, chosen associated attributes, and formulated a string value to be inserted. The Web assistant would either accept or reject the automatic suggestion. Based on the decision, the system could generalise and specialise. Our ontologies would be the attribute hierarchy and the variety of POS tags (in analogy to the semantic and grammatical concepts of [BS00], Figure 1). The following examples illustrate how (in theory) pattern generalisation and pattern specialisation might be used to improve recall and precision of the constructed partial parsing system. Note three abstractions made from the concrete design and implementation: It is assumed that TE patterns are de ned with terminals of both POS and semantics; there is no separation and no parsing of complex tags or whole sentences. It is understood that entity names are also included in the classes of all their predecessor attributes, ie parent, grandparent etc; dierently from the disjoint entity classes of Appendix A.2. Finally, the ontologies displayed in the examples are just very small sub-ontologies.

Example 6.1 (Pattern generalisation) The given ontology is that of Figure 6.11 . The grammar contains these association productions: HumansAttribute ,! FirstPersonPersonalPronoun

ArtVerbGroup HumansNounGroup AnimalsAttribute ,! FirstPersonPersonalPronoun ArtVerbGroup AnimalsNounGroup NatureAttribute ,! FirstPersonPersonalPronoun ArtVerbGroup NatureNounGroup 1 c1 ! c2 stands for \c2 is a c1 "

6.2. FUTURE DIRECTIONS

127

The system discovers I've drawn many castles. It suggests to place the information that the user is interested in drawing castles into the user model attribute Buildings. The Web assistant accepts the suggestion. The automatic training component realises that the above patterns work for all four child attributes of Art objects. It may therefore generalise the patterns to one new production:

ArtobjectsAttribute ,! FirstPersonPersonalPronoun

ArtVerbGroup ArtobjectsNounGroup Art objects

Humans Animals Buildings Nature Figure 6.1: A sub-ontology of user model attributes Generalisations as those of Example 6.1 could improve the recall of our system, as they generate new extraction productions. However, the above situation would never happen in our environment. The IE system only extracts information already represented by a pattern. It does not suggest new patterns. It is no longer the assistant's job to nd new patterns either. Anyway, our domain events parser does already operate in a quite general manner. It does \know" how user model information looks like. What it does not \know" is which of that is in fact not user model information. Too much incorrect information is being extracted. To improve precision, TE patterns need to be made more speci c { something previously tried via association exceptions. Example 6.2 deals with a problem phrase from Section 5.2, Limitations.

Example 6.2 (Pattern specialisation) The given ontology is that of Figure 6.2. The grammar contains this association production:

AnimalsAttribute ,! FirstPersonPersonalPronoun

VerbGroup AnimalsNounGroup

The system discovers I have a cat. It suggests to place the information that the user is interested in drawing cats into the user model attribute Animals. The Web assistant rejects the suggestion. The automatic training component concludes that a VerbGroup is too general for an association of

CHAPTER 6. CONCLUSIONS

128

such phrases with Animals. Thus, it may replace the pattern by specialised productions with each more speci c verb group except the haveVerbGroup:

AnimalsAttribute ,! FirstPersonPersonalPronoun (ArtVerbGroup j WritingVerbGroup

j SkillVerbGroup j InterestVerbGroup j doVerbGroup) AnimalsNounGroup VerbGroup

Art Verb Group

Writing Verb Group

Skill Verb Group

Interest Verb Group

do Verb Group

have Verb Group

Figure 6.2: A sub-ontology of parts of speech By pattern specialisation, automatic training could well contribute to the re nement of association exceptions. By incorporating the attribute hierarchy as an ontology, automatic training would contribute to the formal integration of the user model structure into TE.

Research perspectives The thesis intends to make a useful contribution

to natural language processing, in the sense that it applies shallow IE techniques to the document form of dialogues. Chapter 2 covered the topic of dialogues intensively. However, only basic knowledge was gathered, and deeper research on dialogues from an IE point of view may prove to be interesting and worthwile. A separate evaluation of just the CO results could lead to insights that would improve Algorithm 4.5 and help better understand the nature of references in conversations. Another question in examining dialogues could ask what further roles exist besides users and assistants. What would their sets of conversational circumstances components be? An interdisciplinary investigation of the dialogue concept, in practice and in literature, might yield additional results straightforward enough for utilisation in the existing IE approaches. What further automation can we think of in user modelling? What intelligence can be added to the WAS user modelling tool? One suggestion is a \proactive" modelling tool, which would automatically supply information about the current user, when it is needed by the assistant. Secondly, inference

6.2. FUTURE DIRECTIONS

129

on the user model data is discussed. Inference is the step from extracting just information about a person towards actual knowledge extraction. Previously extracted facts would be interpreted by the system, and conclusions would be drawn based upon them. User model entries would become more and more signi cant and provide more valuable support in consultation. How could user support be further automated? What intelligence can be added to the question answering system? As of now, it retrieves short FAQ answers, depending on a user's query, not yet on his or her user model. The tool might be expanded to a recommender system, retrieving artwork and literature pieces based on a user's stored interests or current requests (possibly falling back on IE techniques again, providing the functionality of an \intelligent library"). It might search for tutorial articles (in Elfwood's \FARP" section) based on the user's stored skills and current problems. This need not be limited to retrieving documents. Intelligent tutorials for Web site usage and developing skills (art and literature in the Elfwood case) might be started. Experts { the assistants { are available for the creation of such course modules. The basis for intelligent tutoring are cognitive models of the learners. Once the user models have become more sophisticated, both in data types and the actual knowledge inserted, they are well available as learner models to base adaptive teaching on. The question answering system could retrieve information, start tutorial programs, or it might even develop its own \consultation dialogue" with the user. If the question answering system was replaced by a knowledge based dialogue system, it could provide automated, interactive responding to a set of standard user questions. Its basis would be expert knowledge on the one hand and gathered user model knowledge on the other. Chapter 1 rashly stated that the advice given by the example assistant to the example user { inspiration for drawing a background to her fairy picture { were something hardly accomplished by a computer system. However, knowledge based systems supporting creative tasks are being researched on, and a variety of commercial products exists. Analysing collaborative problem solving processes happening in the recorded dialogues might lay the groundwork for further contributions to the eld of such \digital assistants" { especially in terms of adaptivity and collaboration. On the other hand, the fundamental idea of WAS is to preserve the human factor in counselling via the Internet. It is apparent from the dialogue logs that the majority of users appreciated in particular the aspect of natural human-human conversation.

Bibliography [AI99]

Douglas E. Appelt and David J. Israel. Introduction to Information Extraction Technology. A Tutorial Prepared for the Sixteenth International Joint Conference on Arti cial Intelligence, Stockholm, Sweden, August 1999.

[AS00]

Johan Aberg and Nahid Shahmehri. The role of human Web assistants in e-commerce: an analysis and a usability study. Internet Research: Electronic Networking Applications and Policy, 10(2):114{125, 2000.

[AS01a]

Cecile Aberg and Nahid Shahmehri. A Multi Application Framework for Automatic Identi cation of Text Patterns. Submitted for publication, 2001.

[AS01b]

Johan Aberg and Nahid Shahmehri. Collection and Exploitation of Expert Knowledge in Web Assistant Systems. In Proceedings of the 34th Hawaii International Conference on Systems Sciences, Maui, Hawaii, USA, January 2001.

[AS01c]

Johan Aberg and Nahid Shahmehri. An Empirical Study of Human Web Assistants: Implications for User Support in Web Information Systems. In Proceedings of the CHI Conference on Human Factors in Computing Systems, Seattle, WA, USA, March 2001.

[ASM01]

Johan Aberg, Nahid Shahmehri, and Dennis Maciuszek. User Modelling for Live Help Systems. In Proceedings of the Second International Workshop on Electronic Commerce, Heidelberg, Germany, November 2001. To be published.

[BMSW97] Daniel Bikel, Scott Miller, Richard Schwartz, and Ralph Weischedel. Nymble: a High-Performance Learning Name- nder. In Proceedings of the Fifth Conference on Applied Natural Language 130

BIBLIOGRAPHY

131

Processing (ANLP-97), pages 194{201, Washington, DC, USA, April 1997.

[BS00]

Cecile Boisson and Nahid Shahmehri. Template Generation for Identifying Text Patterns. In Proceedings of the Twelfth International Symposium on Methodologies for Intelligent Systems { ISMIS 2000, pages 463{473, Charlotte, NC, USA, October 2000.

[CL96]

Jim Cowie and Wendy Lehnert. Information Extraction. Communications of the ACM, 39(1):80{91, January 1996.

[Cun99]

Hamish Cunningham. Information Extraction { a User Guide (updated version). Department of Computer Science, University of Sheeld, UK, April 1999.

[CV01]

Alessandro Cucchiarelli and Paola Velardi. Unsupervised Named Entity Recognition Using Syntactic and Semantic Contextual Evidence. Computational Linguistics, 27(1):123{131, March 2001.

[ECCC94] Stephanie Elzer, Jennifer Chu-Carroll, and Sandra Carberry. Recognizing and Utilizing User Preferences in Collaborative Consultation Dialogues. In Proceedings of the Fourth International Conference on User Modeling, Hyannis, MA, USA, 1994. [FGM93] Christiane Fellbaum, Derek Gross, and Katherine Miller. Adjectives in WordNet (Revised). In Five Papers on WordNet. Cognitive Science Laboratory, Princeton University, NJ, USA, August 1993. [FM99]

Dayne Freitag and Andrew Kachites McCallum. Information Extraction with HMMs and Shrinkage. In AAAI-99 Workshop on Machine Learning for Information Extraction, pages 31{36, Orlando, FL, USA, July 1999.

[FM00]

Dayne Freitag and Andrew McCallum. Information Extraction with HMM Structures Learned by Stochastic Optimization. In AAAI-2000, Austin, TX, USA, August 2000.

[FSM+95] David Fisher, Stephen Soderland, Joseph McCarthy, Fangfang Feng, and Wendy Lehnert. Description of the UMass System as Used for MUC-6. In Proceedings of the Sixth Message Understanding Conference, pages 221{236, 1995.

132 [Gri95]

BIBLIOGRAPHY Ralph Grishman. The NYU System for MUC-6 or Where's the Syntax? In Proceedings of the Sixth Message Understanding Conference, pages 167{175, 1995.

[GWH+95] Robert Gaizauskas, Takahiro Wakao, Kevin Humphreys, Hamish Cunningham, and Yorick Wilks. University of Sheeld: Description of the LaSIE System as Used for MUC-6. In Proceedings of the Sixth Message Understanding Conference, pages 207{220, 1995. [HAB+96] Jerry R. Hobbs, Douglas Appelt, John Bear, David Israel, Megumi Kameyama, Mark Stickel, and Mabry Tyson. FASTUS: A Cascaded Finite-State Transducer for Extracting Information from Natural-Language Text. In Finite State Devices for Natural Language Processing, pages 383{406. MIT Press, Cambridge, MA, USA, 1996. [HGH+98] Kevin Humphreys, Robert Gaizauskas, Chris Huyck, Brian Mitchell, Hamish Cunningham, and Yorick Wilks. University of Sheeld: Description of the LaSIE-II System as Used for MUC-7. In Proceedings of the Seventh Message Understanding Conference, 1998. [Ing97]

Peter Ingels. A Robust Text Processing Technique Applied to Lexical Error Recovery. Licentiate's thesis, Linkoping University, Sweden, January 1997.

[Kas91]

Robert Kass. Building a User Model Implicitely from a Cooperative Advisory Dialog. User Modeling and User-Adapted Interaction, 1(3):203{258, 1991.

[KF88]

Robert Kass and Tim Finin. Modelling the User in Natural Language Systems. Computational Linguistics, 14(3):5{22, 1988.

[Lee97]

Timothy Robert Leek. Information Extraction Using Hidden Markov Models. Master's thesis, University of California, San Diego, USA, 1997.

[Met95]

Marie Meteer et al. Dys uency Annotation Stylebook for the Switchboard Corpus. Linguistic Data Consortium, June 1995. Revised by Ann Taylor.

BIBLIOGRAPHY

133

[MSM93] Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics, 19(2):313{330, 1993. [Rag99] Eva L. Ragnemalm. Student Modelling based on Collaborative Dialogue with a Learning Companion. Dissertation, Linkoping University, Sweden, 1999. [Ril99] Ellen Rilo. Information Extraction as a Stepping Stone toward Story Understanding. In Ashwin Ram and Kenneth Moorman, editors, Understanding Language Understanding: Computational Models of Reading. MIT Press, Montreal, Canada, 1999. [Sch88] Friedemann Schulz von Thun. Storungen und Klarungen, volume 1 of Miteinander Reden. ro ro ro Verlag (Rowohlt), Reinbek, Germany, 1988. In German. [SFL97] Stephen Soderland, David Fisher, and Wendy Lehnert. Automatically Learned vs. Hand-crafted Text Analysis Rules. CIIR Technical Report TC-44, University of Massachusetts, Amherst, MA, USA, 1997. [SSB+ 98] Andreas Stolcke, Elizabeth Shriberg, Rebecca Bates, Noah Coccaro, Daniel Jurafsky, Rachel Martin, Marie Meteer, Klaus Ries, Paul Taylor, and Carol Van Ess-Dykema. Dialog Act Modeling for Conversational Speech. In AAAI Spring Symposium on Applying Machine Learning to Discourse Processing, pages 98{105, Standford, CA, USA, March 1998.

Appendix A Grammar Speci cation A.1 Grammar Rules Vol Personal data (V1) Vol: Attributes: Personal data Personal data is Personal data (V2) Vol: Attributes: Personal data

you can e-mail me at Personal data (V3) Vol: Attributes: Personal data Vol:

(V4) Vol:

You can email me at Personal data You can e-mail me at Personal data

Attributes: Personal data

i'm Number (V5) Vol: Attributes: Age I'm Number (V6) Vol: Attributes: Age Vol:

(V7) Vol:

im only Number i'm only Number

Attributes: Age

I'm only Number (V8) Vol: Attributes: Age I'm not even an Age (V9) Vol: Attributes: Age

134

A.1. GRAMMAR RULES Number years of \practice" Number years of practice Attributes: Age Vol:

(V10) Vol:

I'm a Gender (V11) Vol: Attributes: Gender I'm Country (V12) Vol: Attributes: Country Vol:

(V13) Vol:

i' from Country i'm from Country

Attributes: Country

Vol:

(V14) Vol:

im from Country, Place i'm from Country, Place

Attributes: Country

I'm in Country (V15) Vol: Attributes: Country I am, of course, a Country (V16) Vol: Attributes: Country living in Country (V17) Vol: Attributes: Country Vol:

(V18) Vol:

I don't know if the stores in Country carries Wet I don't know if the stores in Country carry Wet

Attributes: Country

i spent Number Time in Country (V19) Vol: Attributes: Country I took Number Time of Country (V20) Vol: Attributes: Country I'm a Occupation (V21) Vol: Attributes: Occupation I'm only in Occupation (V22) Vol: Attributes: Occupation Vol: I'm so pressed by assignments and stu at Occupation (V23) Attributes: Occupation

I am a Computer by Occupation (V24) Vol: Attributes: Occupation I'm in Number (V25) Vol: Attributes: Occupation

135

APPENDIX A. GRAMMAR SPECIFICATION

136 Name (V26) Vol: Attributes: Name

From Name (V27) Vol: Attributes: Name I'm Name (V28) Vol: Attributes: Name I'm Name...aka Name (V29) Vol: Attributes: Name I'm Name, gallery Number (V30) Vol: Attributes: Name, Link to art Name (my pen name for my Fantasy work) (V31) Vol: Attributes: Name, Fantasy

I ask Conversation style (V32) Vol: Attributes: Conversation style I don't Conversation style often (V33) Vol: Attributes: Conversation style From the Elfwood data (V34) Vol: Attributes: Elfwood data I'm also one of the Elfwood data (V35) Vol: Attributes: Elfwood data

I tired of being an Elfwood data (V36) Vol: Attributes: Elfwood data my Elfwood member (V37) Vol: Attributes: Elfwood member I'm not on Elfwood member (V38) Vol: Attributes: Elfwood member I'm not in Elfwood member yet, would like to be (V39) Vol: Attributes: Elfwood member I have a Elfwood member (V40) Vol: Attributes: Elfwood member i don't have a Elfwood member (V41) Vol: Attributes: Elfwood member i was going to invite you to my neck of Elfwood member (V42) Vol: Attributes: Elfwood member

A.1. GRAMMAR RULES

137

I recently registered for the Elfwood member (V43) Vol: Attributes: Elfwood member I just found Elfwood member, and am trying to join (V44) Vol: Attributes: Elfwood member Link to art (V45) Vol: Attributes: Link to art Vol: Im in Link to art I'm in Link to art (V46) Vol: Attributes: Link to art

My gallery is in Link to art (V47) Vol: Attributes: Link to art Vol: Galler Number Gallery Number (V48) Vol: Attributes: Link to art

Vol:

(V49) Vol:

my gallery number Number my gallery's number Number

Attributes: Link to art

I am in gallery Number (V50) Vol: Attributes: Link to art Vol:

(V51) Vol:

I'm in lothNumber and Elfwood member I'm in lothlorien Number and Elfwood member

Attributes: Link to art, Elfwood member

I am really bad at Art (V52) Vol: Attributes: Art I'm no great shakes as an Art (V53) Vol: Attributes: Art Vol:

(V54) Vol:

i'e just started to Art i've just started to Art

Attributes: Art

(V55)

Vol:

I started Art in like late Number Occupation or early Number

Vol:

I 'm an Art I'm an Art

Occupation Attributes: Art

(V56) Vol:

Attributes: Art

Vol:

(V57) Vol:

I usually draw best a Time........like Time I usually draw best at Time, like Time

Attributes: Art

APPENDIX A. GRAMMAR SPECIFICATION

138

I have to have Extended or the Extended on when i draw (V58) Vol: Attributes: Art I make more Art media than Pictures (V59) Vol: Attributes: Art media i have all but abandoned Wet since i discovered Digital (V60) Vol: Attributes: Wet, Digital

I like Oil paint (V61) Vol: Attributes: Oil paint I don't use Watercolour (V62) Vol: Attributes: Watercolour

I made an Acrylics of Characters (V63) Vol: Attributes: Acrylics I'm married to my Pencil (V64) Vol: Attributes: Pencil I often use a Pencil, for Extended at Occupation (V65) Vol: Attributes: Pencil, Occupation

(V66)

Vol:

I can't really draw with Pencil, but I have to use Pencil at Occupation for Extended

Attributes: Pencil, Occupation

I work with Pencil and Art media (V67) Vol: Attributes: Pencil, Art media I also do Pencil Sketching (V68) Vol: Attributes: Pencil, Sketching Coloured pencil (V69) Vol: Attributes: Coloured pencil I'm using a Digital (V70) Vol: Attributes: Digital

I'm into Digital (V71) Vol: Attributes: Digital Vol:

(V72) Vol:

im a lefty with a right-handed Digital i'm a left-handed with a right-handed Digital

Attributes: Digital

Vol: (V73) Vol: Attributes:

i dont have the Art i want to do everything on the Computer i don't have the Art i want to do everything on the Computer Digital

A.1. GRAMMAR RULES (V74)

Vol:

139

im less nervy about trying new things with Adobe Photo-

shop Attributes: Adobe Photoshop

Waiting to get a Graphics tablets (V75) Vol: Attributes: Graphics tablets I draw some Art objects (V76) Vol: Attributes: Art objects

Vol:

(V77) Vol:

I good at Art objects I am good at Art objects

Attributes: Art objects

i drew some better Humans (V78) Vol: Attributes: Humans

(V79)

Vol:

It would be better if I could draw Humans, and Humans, and Humans, instead of Animals like my Animals

Attributes: Humans, Animals

I actually have one Nature Pictures up (V80) Vol: Attributes: Nature Vol:

(V81) Vol:

im a Colour junkie i'm a Colour junkie

Attributes: Art styles

Vol:

(V82) Vol:

I actually learned how to draw through Art styles I actually learnt how to draw through Art styles

Attributes: Art styles

I knew Art techniques (V83) Vol: Attributes: Art techniques I do a lot of Sketching (V84) Vol: Attributes: Sketching

I've been Sketching my whole life (V85) Vol: Attributes: Sketching I'm pretty much a Sketching only artist (V86) Vol: Attributes: Sketching I have a few Sketching of Art objects (V87) Vol: Attributes: Sketching, Art objects

(V88)

Vol:

i actually tried just Sketching like Anime/Manga Humans and

Humans Attributes: Sketching, Anime/Manga, Humans

APPENDIX A. GRAMMAR SPECIFICATION

140

i do my Sketching on paper and then Scanners (V89) Vol: Attributes: Sketching, Scanners I love drawing Detail drawing Humans (V90) Vol: Attributes: Detail drawing, Humans I'm a Writing (V91) Vol: Attributes: Writing I Writing in my spare time (V92) Vol: Attributes: Writing I Writing myself, nothing published (V93) Vol: Attributes: Writing

(V94)

Vol:

I'm a Writing who has just started to make the leap into the Art world

Attributes: Writing, Art

My stu is mainly traditional, Fantasy with Characters (V95) Vol: Attributes: Fantasy, Characters I write Stories (V96) Vol: Attributes: Writing technical i've written Stories for Occupation (V97) Vol: Attributes: Writing technical I normally do Stories (V98) Vol: Attributes: Writing technical I've currently got about Number Stories in the works (V99) Vol: Attributes: Writing technical I'll stick with the goal of completing a Stories (V100) Vol: Attributes: Writing technical I just can't seem to stick to one Stories (V101) Vol: Attributes: Writing technical

(V102) (V103)

Vol:

I have completed the rst Stories of my Stories and have been rejected by Number Writing

Attributes: Writing technical, Writing Vol:

Most of Stories are Fantasy, but a few are Sci-Fi (written as Name) and one of Writing technical is Horror (written as Name)

Attributes: Writing technical, Fantasy, Sci-Fi, Name, Horror

I like Anime/Manga Pictures (V104) Vol: Attributes: Pictures

A.1. GRAMMAR RULES

141

I particularly like the look of Animals (V105) Vol: Attributes: Pictures Vol:

(V106) Vol:

i have seen Name actually..and i like Namework i have seen Name actually, and i like Namework

Attributes: Pictures

I collect Art of Humans (V107) Vol: Attributes: Pictures Vol:

(V108) Vol:

I collect Art of Humans..mainly Humans with Colour Humans I collect Art of Humans, mainly Humans with Colour Humans

Attributes: Pictures

i may buy a Pictures (V109) Vol: Attributes: Pictures we found our last Pictures here (V110) Vol: Attributes: Pictures Vol: neither used an Elfwood data nor Internet (V111) Attributes: User functions, Internet

I use Internet (V112) Vol: Attributes: Internet love my Internet (V113) Vol: Attributes: Internet I'm too used to Internet and Internet (V114) Vol: Attributes: Internet I don't Internet (V115) Vol: Attributes: Internet I have no work Internet (V116) Vol: Attributes: Internet Vol: We'd really like new users for our Internet and Internet (V117) Attributes: Internet Internet Number I'm coming from (V118) Vol: Attributes: Internet

I have no Scanners (V119) Vol: Attributes: Scanners Vol:

(V120) Vol:

dont have a Scanners don't have a Scanners

Attributes: Scanners

APPENDIX A. GRAMMAR SPECIFICATION

142 Vol:

(V121) Vol:

I dont have a Scanners I don't have a Scanners

Attributes: Scanners

I have only a medium sized Scanners (V122) Vol: Attributes: Scanners Vol:

(V123) Vol:

I havent goten around to buying a Scanners I haven't gotten around to buying a Scanners

Attributes: Scanners

Scanners is busted (V124) Vol: Attributes: Scanners

I got my Scanner with Digital (V125) Vol: Attributes: Scanner, Digital Vol:

(V126) Vol:

I use MS WindowsNumber I use MS Windows Number

Attributes: MS Windows

i love Extended (V127) Vol: Attributes: Extended I'm coming from Extended (V128) Vol: Attributes: Extended I've been Extended quite a lot (V129) Vol: Attributes: Extended I collect Art styles (V130) Vol: Attributes: Extended Vol:

(V131) Vol:

i rpg in Internet i roleplay in Internet

Attributes: Extended

(V132) (V133)

Vol:

I'm an Title Extended myself, with a little Title and Title thrown in

Attributes: Extended

I see Name, Name and Name (though I'm yet to open one Stories of Name) as leaders Attributes: Extended Vol:

A.1. GRAMMAR RULES

143

Vol-Back

my webpage Personal data (VB1) Vol: Attributes: Personal data I'm from Place, so Country is as good a bet as Country (VB2) Vol: Attributes: Country I can speak some Country (VB3) Vol: Attributes: Country I'm in a Occupation (VB4) Vol: Attributes: Occupation i have Occupation (VB5) Vol: Attributes: Occupation I'm supposed to be an Elfwood data (VB6) Vol: Attributes: Elfwood data Vol: This is Name: Elfwood member with Writing (VB7) Attributes: Name, Elfwood member, Writing my Elfwood member (VB8) Vol: Attributes: Elfwood member

I'm a Elfwood member (VB9) Vol: Attributes: Elfwood member I'm new to Elfwood member (VB10) Vol: Attributes: Elfwood member Vol:

(VB11) Vol:

I got into Elfwood member just reciently I got into Elfwood member just recently

Attributes: Elfwood member

a bunch of my Stories were uploaded (VB12) Vol: Attributes: Elfwood member

(VB13)

Vol:

I lled out an application to put some of my Art on Elfwood

member Attributes: Elfwood member, Art

Link to stories (VB14) Vol: Attributes: Link to stories i have no idea about Art (VB15) Vol: Attributes: Art

Vol: I just recently discovered that i have Art (VB16) Attributes: Art

APPENDIX A. GRAMMAR SPECIFICATION

144 Vol:

(VB17) Vol:

i kinda stopped Art for like Number Time i kind of stopped Art for like Number Time

Attributes: Art

my deep and horrible lack of Art (VB18) Vol: Attributes: Art Back:

(VB19) Vol:

topic category: Art category Art category's not a problem

Attributes: Art Back:

(VB20) Vol:

topic category: Art category the Art category are good, the Art category suck

Attributes: Art Back:

(VB21) Vol:

topic category: Art category I just don't have the patience to nish Art category

Attributes: Art Vol:

(VB22) Vol:

i have gotten a number of Art for Humans i have got a number of Art for Humans

Attributes: Art, Humans

Art media-that's exactly what I use Art media { that's exactly what I use Attributes: Art media Vol:

(VB23) Vol:

I have a set of Ink but I don't know anything about Ink (VB24) Vol: Attributes: Ink

(VB25) (VB26)

Vol:

I have some Ink and almost no knowledge of how to use

Vol:

I do know that Ink are bamboo and can't be left sitting in water

Ink Attributes: Ink

Attributes: Ink Vol:

(VB27) Vol:

I will put to much Ink or not enough I will put too much Ink or not enough

Attributes: Ink Vol:

(VB28) Vol:

the Ink I use for mosy of my Art { as an Art techniques the Ink I use for most of my Art { as an Art techniques

Attributes: Ink, Art techniques

I usually just screw everything with Watercolour (VB29) Vol: Attributes: Watercolour Watercolour's much easier to work with (VB30) Vol: Attributes: Watercolour

A.1. GRAMMAR RULES

145

i have a tube of Watercolour (VB31) Vol: Attributes: Watercolour

(VB32)

I love to Watercolour and I think I'm pretty good at Watercolour, but I can never actually Art something to Wet Attributes: Watercolour, Dry Vol:

Vol:

(VB33) Vol:

I have a great time Wet things...Watercolour is so much fun...BUT, I lack to Art the things I want to Wet I have a great time Wet things, Watercolour is so much fun, BUT I lack to Art the things I want to Wet

Attributes: Watercolour, Dry

the hard part for me is the pre-Wet Art (VB34) Vol: Attributes: Dry I usually use a Pencil or an Pencil (VB35) Vol: Attributes: Pencil i have a slender motion with my Pencil (VB36) Vol: Attributes: Pencil

(VB37) (VB38)

Vol:

I just don't have the Pencil skill to Art techniques something on paper

Attributes: Pencil, Art techniques Vol:

I somehow can't draw very well with a Pencil or Pencil, I only use Pencil for Perspective

Attributes: Pencil, Perspective Vol:

(VB39) Vol:

Not Coloured pencil though bad experiences Not Coloured pencil though, bad experiences

Attributes: Coloured pencil

I haven't really ever used a Digital extensively (VB40) Vol: Attributes: Digital I can't even use Digital correctly (VB41) Vol: Attributes: Digital I don't know what half of Digital do (VB42) Vol: Attributes: Digital Vol:

(VB43) Vol:

I got some serious problems guring out how DigitalNumber or Digital is working I have got some serious problems guring out how Digital Number or Digital is working

Attributes: Digital

(VB44)

Vol:

getting Digital to work like I want Digital to has been a problem

Attributes: Digital

APPENDIX A. GRAMMAR SPECIFICATION

146

I use Adobe Photoshop (VB45) Vol: Attributes: Adobe Photoshop I just got Adobe Photoshop (VB46) Vol: Attributes: Adobe Photoshop I have Adobe Photoshop Number (VB47) Vol: Attributes: Adobe Photoshop I don't have Adobe Photoshop (VB48) Vol: Attributes: Adobe Photoshop Vol: (VB49) Vol: Attributes:

I don't have access to Adobe Photoshop on my own comp I don't have access to Adobe Photoshop on my own computer Adobe Photoshop

I can't seem to use Adobe Photoshop correctly (VB50) Vol: Attributes: Adobe Photoshop I know almost nothing about Adobe Photoshop (VB51) Vol: Attributes: Adobe Photoshop Back:

(VB52) Vol:

topic category: Adobe Photoshop I know how to do really basic Adobe Photoshop, like Art

Attributes: Adobe Photoshop

(VB53) (VB54)

I could do a Pictures in Adobe Photoshop or do an Ink Art techniques Attributes: Adobe Photoshop, Ink, Art techniques Vol:

Vol:

I use Adobe Photoshop to Art techniques, Adobe Photoshop's on a Computer

Attributes: Adobe Photoshop, Art techniques, Computer

Vol: I can't even get Paintshop Pro to paint on the right Digital (VB55) Attributes: Paintshop Pro

my 3D programs (VB56) Vol: Attributes: 3D programs I'm having trouble drawing Art objects (VB57) Vol: Attributes: Art objects I'm having problems drawing Art objects (VB58) Vol: Attributes: Art objects

I can't think of any good ideas for Art objects (VB59) Vol: Attributes: Art objects

A.1. GRAMMAR RULES

147

my Art objects never look right (VB60) Vol: Attributes: Art objects Art objects do seem a bit short (VB61) Vol: Attributes: Art objects Vol:

(VB62) Vol:

the entire Art objects thing the entire Art objects thing doesn't work well for me the entire Art objects thing doesn't work well for me

Attributes: Art objects Back: Vol:

(VB63) Vol:

topic category: Art category my Art category is not realy as Sci-Fi as most Is alot of Fantasy but Art category wasent Fantasy my Art category is not really as Sci-Fi as most, my Art category is a lot of Fantasy, but my Art category wasn't Fantasy

Attributes: Art objects

(VB64)

Vol:

I am interested in adding more dramatic Art objects to my

Humans Attributes: Art objects, Humans

Humans're hard (VB65) Vol: Attributes: Humans

I really SUCK at the Humans (VB66) Vol: Attributes: Humans I fail at drawing the Humans (VB67) Vol: Attributes: Humans can't draw Humans very well (VB68) Vol: Attributes: Humans Vol:

(VB69) Vol:

i can only draw Humans good i can only draw Humans well

Attributes: Humans

Humans, Humans I can't (VB70) Vol: Attributes: Humans Vol: I seem to have totally avoided learning about Humans (VB71) Attributes: Humans Vol:

(VB72) Vol:

i have troubles with the Humans...and Humans i have trouble with the Humans...and Humans

Attributes: Humans

Vol: I'm having trouble with my Humans's Humans and Humans (VB73) Attributes: Humans

APPENDIX A. GRAMMAR SPECIFICATION

148

i have problems with the Humans (VB74) Vol: Attributes: Humans Vol:

(VB75) Vol:

i'm having problem drawing Humans i'm having problems drawing Humans

Attributes: Humans

the special problem are the Humans (VB76) Vol: Attributes: Humans Vol: the special problem I am having are Humans (VB77) Attributes: Humans Vol:

(VB78) Vol:

I draw and hve a problem getting the Humans right I draw and have a problem getting the Humans right

Attributes: Humans

(VB79) (VB80)

Vol:

I know the rest of the Humans, all but the Humans and

Vol:

Humans always look too big or one Humans is fatter than

Humans Attributes: Humans

another

Attributes: Humans

Vol: My Humans which I tried to draw was similar Vol: My Humans which I tried to draw were similar (VB81) Attributes: Humans

the placement of the Humans never seems to suit me (VB82) Vol: Attributes: Humans Vol:

(VB83) Vol:

I've managed to cope with my other inabilities (eg, Humans) by covering Humans with stu I've managed to cope with my other inabilities (for example, Humans) by covering Humans with stu

Attributes: Humans Vol:

(VB84) Vol:

I'm really just begining to draw Humans/Art objects with any real success I'm really just beginning to draw Humans/Art objects with any real success

Attributes: Humans, Art objects

I tried a Animals (VB85) Vol: Attributes: Animals Vol: (VB86) Attributes: Vol: Attributes:

i am just know getting into Animals Animals i am just now getting into Animals Animals

A.1. GRAMMAR RULES Vol:

(VB87) Vol:

149

I can't nd out how to draw Animals, or Animals I can't nd out how to draw Animals, or Animals

Attributes: Animals Vol:

(VB88) Vol:

i cant really put Animals i can't really put Animals

Attributes: Animals Vol:

(VB89) Vol:

i can even start to draw Animals i can't even start to draw Animals

Attributes: Animals Vol:

(VB90) Vol:

i cant seem to ever get Animals started i can't seem to ever get Animals started

Attributes: Animals

Animals are no problem neither are Animals Animals are no problem, neither are Animals Attributes: Animals Vol:

(VB91) Vol: Vol:

(VB92) Vol:

anytime I try to draw a Animals Art techniques, a Animals look to Humans or not like a Animals at all anytime I try to draw a Animals Art techniques, a Animals looks too Humans or not like a Animals at all

Attributes: Animals

I love Animals art (VB93) Vol: Attributes: Animals Vol: i like Animals to look majestic, huge (VB94) Attributes: Animals

(VB95)

Vol:

I have a hard time drawing Animals, especially when they are Perspective

Attributes: Animals, Perspective Vol:

(VB96) Vol:

mine Animals seem so Perspective,not a lot of Perspective, and Art objects isnt very good my Animals seem so Perspective, not a lot of Perspective, and Art objects isn't very good

Attributes: Animals, Perspective, Art objects

I don't have any problems with Nature (VB97) Vol: Attributes: Nature Vol:

(VB98) Vol:

I am having troubles trying to draw the Nature of Nature I am having trouble trying to draw the Nature of Nature

Attributes: Nature

I love Colour (VB99) Vol: Attributes: Art styles

APPENDIX A. GRAMMAR SPECIFICATION

150

I'm trying out the Art styles thing (VB100) Vol: Attributes: Art styles I would like to start out on drawing Art styles (VB101) Vol: Attributes: Art styles Vol:

(VB102) Vol:

I learned how to draw through Art styles, so I never got very good Humans lessons I learnt how to draw through Art styles, so I never got very good Humans lessons

Attributes: Art styles, Humans

(VB103)

Vol:

i used to only draw Art styles Humans but now i want Realism looking stu

Attributes: Art styles, Humans, Realism

I tried to make a Art styles to one of my Stories (VB104) Vol: Attributes: Art styles, Writing technical Vol: one of the things that dont make my Pictures look Realism is the Humans one of the things that don't make my Pictures look Realism (VB105) Vol: is the Humans Attributes: Realism, Humans

I just started Anime/Manga (VB106) Vol: Attributes: Anime/Manga Vol: Attributes: (VB107) Vol: Attributes: Vol: Attributes:

I have gotton into Anime/Manga Anime/Manga I have gotten into Anime/Manga Anime/Manga I have got into Anime/Manga Anime/Manga

I'm also having trouble with Anime/Manga (VB108) Vol: Attributes: Anime/Manga I am not very good at Anime/Manga and Realism (VB109) Vol: Attributes: Anime/Manga, Realism I mainly do Realism (VB110) Vol: Attributes: Realism my Art techniques always looks ugly (VB111) Vol: Attributes: Art techniques Back: topic category: Art category Vol: I try to mimic Art category...Art category come out the best (VB112) Vol: I try to mimic Art category, Art category come out the best Attributes: Art techniques

A.1. GRAMMAR RULES (VB113)

151

Vol:

I always have problems Art techniques my Pictures with the

Vol:

I cant Perspective I can't Perspective

Computer Attributes: Art techniques, Digital

(VB114) Vol:

Attributes: Perspective

I'm not too good with Perspective (VB115) Vol: Attributes: Perspective i also have a problem with Perspective (VB116) Vol: Attributes: Perspective I also have problems with Perspective (VB117) Vol: Attributes: Perspective Vol:

(VB118) Vol:

ijust seem to have a lot of trouble with Perspective and i just seem to have a lot of trouble with Perspective and Perspective

Perspective Attributes: Perspective

Vol: Vol: (VB119) Vol: Attributes: Vol:

(VB120) Vol:

Perspective i need alot of pratice on Perspective i need a lot of practice on Perspective i need a lot of practise on Perspective

I know about a Perspective, but I nd my Art way to lightly I know about a Perspective, but I nd my Art way too lightly Perspective

Perspective Attributes: Perspective

Vol:

(VB121) Vol:

my problim is more on the lines of not making my Pictures look like its outing my problem is more on the lines of not making my Pictures look like it's oating

Attributes: Perspective

Art objects turns out bad... cause of the Perspective Art objects turns out bad because of the Perspective Attributes: Perspective

Vol:

(VB122) Vol:

I have trouble drawing Perspective of Humans (VB123) Vol: Attributes: Perspective, Humans I've never been very good at Sketching (VB124) Vol: Attributes: Sketching

APPENDIX A. GRAMMAR SPECIFICATION

152 Vol:

(VB125) Vol:

I need to learn how (or something!) to Sketching things out better so that I have somethng to Wet I need to learn how (or something!) to Sketching things out better so that I have something to Wet

Attributes: Sketching, Wet Vol:

(VB126) Vol:

Im really good at Sketching Humans, and Buildings, but when it comes to trying to dry something Art techniques, I can't get something Art techniques on paper I'm really good at Sketching Humans and Buildings, but when it comes to trying to draw something Art techniques, I can't get something Art techniques on paper

Attributes: Sketching, Humans, Buildings, Art techniques

(VB127)

Vol:

I did some Sketching of Animals semi Humans semi Animals but they always turned out too Animals-like

Attributes: Sketching, Animals, Humans

I usually wind up doing lots of Detail drawing (VB128) Vol: Attributes: Detail drawing I'm usually a Detail drawing fanatic (VB129) Vol: Attributes: Detail drawing I have Writing (VB130) Vol: Attributes: Writing

I have the worst case of Writing (VB131) Vol: Attributes: Writing Vol:

(VB132) Vol:

I wanna start Writing I want to start Writing

Attributes: Writing

I'm considering Writing (VB133) Vol: Attributes: Writing

(VB134)

Vol:

Aside of not Writing something since I was Number, I don't consider myself a Skill level

Attributes: Writing

my Writing on Elfwood member (VB135) Vol: Attributes: Writing, Elfwood member Vol:

(VB136) Vol:

I've written, but only Serious writing I have written, but only Serious writing

Attributes: Serious writing

i can't write Stories (VB137) Vol: Attributes: Writing technical

A.1. GRAMMAR RULES (VB138)

Vol:

153

I've been trying to write Stories but all my ideas turn into something of a Stories

Attributes: Writing technical

Writing itself is not my biggest of problem { it's Writing technical Writing itself is not my biggest of problems { it's Writing (VB139) Vol: technical Attributes: Writing technical Vol:

my Stories, Title (VB140) Vol: Attributes: Writing technical I am before my rst Stories (VB141) Vol: Attributes: Writing technical Vol:

(VB142) Vol:

I want tomhave my rst try on Stories I want to have my rst try on Stories

Attributes: Writing technical

Back:

(VB143) Vol:

topic category: Literature category i like to do Literature category in detail

Attributes: Writing technical

Vol:

(VB144) Vol:

I nd Stories a bit too... plain, un nished I nd Stories a bit too plain, un nished

Attributes: Writing technical

I have these characters for a Stories...they're Characters (VB145) Vol: Attributes: Writing technical, Characters Vol: the Stories's going to be Plot, and I've got Number pages (VB146) Attributes: Writing technical, Plot Vol:

(VB147) Vol:

I've got an Grammar I've got the Grammar

Attributes: Grammar

Vol:

(VB148) Vol:

I'm currently translating one of my Fantasy-Stories into Country (I originally wrote it in Country) I'm currently translating one of my Fantasy Stories into Country (I originally wrote it in Country)

Attributes: Grammar, Fantasy, Writing technical, Country Back:

(VB149) Vol:

in query: Wyvern's Library I choose to portray Characters

Attributes: Characters

APPENDIX A. GRAMMAR SPECIFICATION

154 Vol:

(VB150) Vol:

im collecting information about Fantasy Characters for a Extended which Number of my friends and i am programming i'm collecting information about Fantasy Characters for a Extended which Number of my friends and i are programming

Attributes: Characters, Computer

I'm stuck on the concept of Setting and Setting (VB151) Vol: Attributes: Setting

(VB152)

Vol:

I am working on creating a Setting of semi Humans semi

Animals Humans Attributes: Setting

my problem is with Plot (VB153) Vol: Attributes: Plot I'm having trouble getting started on a main Plot (VB154) Vol: Attributes: Plot I have a Stories written in Number of Perspective (VB155) Vol: Attributes: Point of view I don't know about Intranet enough (VB156) Vol: Attributes: Intranet my Tour creation (VB157) Vol: Attributes: Tour creation Stories on my Tour creation (VB158) Vol: Attributes: Tour creation Stories for my Tour creation (VB159) Vol: Attributes: Tour creation

When I do use Adobe Photoshop I upload from the Computer (VB160) Vol: Attributes: Picture upload Vol:

(VB161) Vol:

Its a pain User functions It's a pain User functions

Attributes: User functions Vol:

(VB162) Vol:

i have been coming her for about NumberTime..but only doing the User functions i have been coming here for about Number Time, but only doing the User functions

Attributes: User functions

A.1. GRAMMAR RULES Vol:

(VB163) Vol:

155

Our organization entered a link to be submitted to FantasyHoo Our organisation entered a link to be submitted to Fanta-

syHoo Attributes: FantasyHoo

I'm not as familiar with Computer (VB164) Vol: Attributes: Computer I have little idea of Internet (VB165) Vol: Attributes: Internet Vol:

(VB166) Vol:

I'm use to doing things with Internet I'm used to doing things with Internet

Attributes: Internet

Vol: I'm pirating Internet time from Place (VB167) Attributes: Internet

i am forming a new Internet (VB168) Vol: Attributes: Internet I never used Internet on the Computer before (VB169) Vol: Attributes: Internet, Computer I'm normally just an average Internet nut and Extended (VB170) Vol: Attributes: Internet, Extended my Scanners doesn't scan well (VB171) Vol: Attributes: Scanners I can only use my Scanners program (VB172) Vol: Attributes: Scanners Vol:

(VB173) Vol:

i ve only read Title i've only read Title

Attributes: Extended

Vol:

(VB174) Vol:

i read so much more Stories than Stories i read so many more Stories than Stories

Attributes: Extended

(VB175)

Vol:

I've seen the movie Title and I liked the background of

Title Attributes: Extended

play all sorts of Extended from Extended to Extended (VB176) Vol: Attributes: Extended

APPENDIX A. GRAMMAR SPECIFICATION

156 Back: Vol: (VB177) Vol:

topic category: Literature category I've Extended enough and aquired enough Literature category I've Extended enough and acquired enough Literature cate-

gory Attributes: Extended, Writing

Q-A

Q:

tell me your address

Q: (QA2) Q: A: Attributes:

what's yer e-mail what's your e-mail Email to Personal data

Q: (QA3) Q: A: Attributes:

what's you mail addy what's your mail address

(QA1) A:

Personal data Attributes: Personal data

Personal data Personal data Personal data

Q:

have a Personal data

Q:

do you have an Personal data

Q: Q: (QA6) A: A: Attributes:

you got an Personal data have you got an Personal data yep Personal data yes, Personal data

(QA4) A:

Personal data Attributes: Personal data

(QA5) A:

Personal data Attributes: Personal data

Personal data

Q:

You have any work online

Q:

You have any work online go to Personal data

(QA7) A:

Personal data Attributes: Personal data

(QA8) A:

Attributes: Personal data Q:

tell me what your Personal data is

Q:

tell me what your Personal data is Personal data's called Title

(QA9) A:

Personal data Attributes: Personal data

(QA10) A:

Attributes: Personal data

A.1. GRAMMAR RULES Q: (QA11) A: A: Attributes:

have you been Occupation no i haven't no, i haven't

Q: A: (QA12) A: Attributes:

you're a Occupation or Yes, I'm a Occupation, Number year old Yes, I'm a Occupation, Number years old

Occupation

Occupation, Age

Q:

who are you

Q:

who are you I'm Name, also known as Name

(QA13) A:

Name Attributes: Name

(QA14) A:

Attributes: Name

(QA15) A:

name please Name is my name

Q:

what's your name

Q:

Attributes: Name

(QA16) A:

Name Attributes: Name

Q: (QA17) Q: A: Attributes:

whats your name what's your name Name Name

(QA18) A:

what's your name Name, or Name

(QA19) A:

Q:

what's your name my name is Name

Q:

what's your name My name's Name

Q:

Attributes: Name Attributes: Name

(QA20) A:

Attributes: Name

Q: (QA21) Q: A: Attributes: Q:

(QA22) A:

Whats your name What's your name Name Name

What's your name it's Name

Attributes: Name

157

APPENDIX A. GRAMMAR SPECIFICATION

158 Q: (QA23) A: A: Attributes: Q:

(QA24) A:

What's your name Name is Human..My real name is Name Name is Human, my real name is Name Name

your name was My name is Name

Attributes: Name Q:

may I ask your name

(QA26) A:

Q:

may I ask your name name is Name

Q:

May I ask your name my name is Name

(QA25) A:

Name Attributes: Name

Attributes: Name

(QA27) A:

Attributes: Name Q:

may I rst ask your name

Q:

can I ask your name

Q:

can I have your name/alias

(QA31) A:

Q:

You still haven't told me your name Name is the name, from Country

Q: (QA32) A: A: Attributes:

do you have a Elfwood member Name...gal.Number Name, gallery Number

(QA28) A:

Name Attributes: Name

(QA29) A:

Name Attributes: Name

(QA30) A:

Name Attributes: Name

Attributes: Name, Country

Name, Link to art

(QA33) A:

Do you have an Elfwood member I'm Name in gallery Number

Q: (QA34) A: A: Attributes:

Are you an Elfwood member Name L Number ZNumber Name, Lothlorien Number, Zone Number

Q:

Attributes: Name, Link to art

Name, Link to art

A.1. GRAMMAR RULES Q: A: (QA35) A: Attributes: Q:

(QA36) A:

Might I ask if you have art in Elfwood member yes...Name. gal.Number yes, Name, gallery Number Name, Link to art

do you mind if I have a look at your work I'm Name in gallery Number

Attributes: Name, Link to art Q:

(QA37) A:

aren't you an Elfwood data yes

Attributes: Elfwood data Q:

(QA38) A:

your Elfwood member I don't have an Elfwood member

Attributes: Elfwood member

(QA39) A:

are you a Elfwood member yes

(QA40) A:

Q:

are you a Elfwood member Yes

Q: (QA41) A: A: Attributes:

are you a Elfwood member Yep, I am a Elfwood member Yes, I am a Elfwood member

Q: (QA42) A: A: Attributes:

are you a Elfwood member Yes I am an Elfwood member Yes, I am an Elfwood member

Q:

Attributes: Elfwood member Attributes: Elfwood member

Elfwood member

Elfwood member

(QA43) A:

are you a Elfwood member I'm not a Elfwood member

Q: (QA44) A: A: Attributes:

are you a Elfwood member nope, not a Elfwood member no, not a Elfwood member

Q: (QA45) A: A: Attributes:

are you a Elfwood member Dont think so Don't think so

Q:

Attributes: Elfwood member

Elfwood member

Elfwood member

159

APPENDIX A. GRAMMAR SPECIFICATION

160 (QA46) A:

Are you a Elfwood member no

Q: Q: (QA47) A: A: Attributes:

are you a Elfwood member are you an Elfwood member I dont think so I don't think so

Q:

Attributes: Elfwood member

Elfwood member

(QA48) A:

are you an Elfwood member Yes

(QA49) A:

Q:

are you an Elfwood member I am

Q: (QA50) A: A: Attributes:

Are you an Elfwood member yup....a newbie yes, a newbie

Q:

Attributes: Elfwood member

Attributes: Elfwood member

Elfwood member

(QA51) A:

Are you an Elfwood member I am an Elfwood member

Q:

Are you an Elfwood member No...a visitor

Q:

Attributes: Elfwood member

(QA52) A:

Attributes: Elfwood member

(QA53) A:

are you already a Elfwood member I was accepted

(QA54) A:

Q:

are you already a Elfwood member I am not uploaded yet

Q:

whether you are a Elfwood member I'm not a Elfwood member

Q:

Attributes: Elfwood member

Attributes: Elfwood member

(QA55) A:

Attributes: Elfwood member

(QA56) A:

you have an Elfwood member yes

Q: (QA57) Q: A: Attributes:

you got a Elfwood member have you got a Elfwood member No, but I'm trying to get in

Q:

Attributes: Elfwood member

Elfwood member

A.1. GRAMMAR RULES Q: (QA58) Q: A: Attributes:

you got a Elfwood member have you got a Elfwood member not yet

Q: (QA59) Q: A: Attributes:

you got a Elfwood member have you got a Elfwood member I applied

Q: Q: (QA60) A: A: Attributes:

you got a Elfwood member have you got a Elfwood member im still waiting i'm still waiting

Q: (QA61) A: A: Attributes:

you've got a Elfwood member Yep Yes

Elfwood member

Elfwood member

Elfwood member

Elfwood member

(QA62) A:

you've got a Elfwood member Not yet

(QA63) A:

Q:

you've got a Elfwood member I applied

Q: (QA64) A: A: Attributes:

you want a Elfwood member nope, got a Elfwood member no, got a Elfwood member

Q:

Attributes: Elfwood member Attributes: Elfwood member

Elfwood member

(QA65) A:

Are you going to join Elfwood member I'm already a Elfwood member

Q: Q: (QA66) A: A: Attributes:

where's your gallery...on Elfwood member where's your gallery, on Elfwood member Yep Yes

Q:

Attributes: Elfwood member

Q:

(QA67) A:

Elfwood member

May I have the link to your gallery no on Elfwood member

Attributes: Elfwood member Q:

(QA68) A:

Do you have Elfwood member i do have a page, but it is not up

Attributes: Elfwood member

161

APPENDIX A. GRAMMAR SPECIFICATION

162 (QA69) A:

do you have a Elfwood member Yes I have

Q:

do you have a Elfwood member yes, I'm a Elfwood member

Q:

Attributes: Elfwood member

(QA70) A:

Attributes: Elfwood member

Q: (QA71) A: Attributes:

do you have a Elfwood member I'm an Art at Elfwood member, but I don't have a Elfwood

member Elfwood member

(QA72) A:

do you have an Elfwood member No, I don't

Q: (QA73) A: A: Attributes:

do you have a Elfwood member Nope, not yet No, not yet

Q:

Attributes: Elfwood member

Q:

(QA74) A:

Elfwood member

do you have an Elfwood member I'm planning on getting an Elfwood member

Attributes: Elfwood member

(QA75) A:

Do you have a Elfwood member no

Q:

Do you have a Elfwood member No

Q:

Attributes: Elfwood member

(QA76) A:

Attributes: Elfwood member Q:

(QA77) A:

do you personally have a Elfwood member I have a Elfwood member

Attributes: Elfwood member Q:

(QA78) A:

Do you use Elfwood member often I'm a Elfwood member

Attributes: Elfwood member

(QA79) A:

Do you have Art in Elfwood member no i don't have Art in Elfwood member

Q: A: (QA80) A: Attributes:

may I ask if you have a Elfwood member no, i dont have a Elfwood member no, i don't have a Elfwood member

Q:

Attributes: Elfwood member

Elfwood member

A.1. GRAMMAR RULES Q:

(QA81) A:

163

May I ask where you are

Elfwood member Attributes: Elfwood member

Art or Elfwood member Art (Fantasy) Attributes: Elfwood member Q:

(QA82) A:

Q: (QA83) A: A: Attributes:

Have you been accepted in Elfwood member i havent tried i haven't tried Elfwood member

(QA84) A:

Do you have Writing online I'm in Elfwood member, as Name (Name)

Q: (QA85) A: A: Attributes:

Do you have a Elfwood member Yes, Username Name Yes, user name Name

Q:

Attributes: Elfwood member, Name

Q: A:

(QA86) A:

Elfwood member, Name

do you have a Elfwood member No, I don't, I haven't really done a lot of Fantasy Art that's wortzh showing No, I don't, I haven't really done a lot of Fantasy Art that's worth showing

Attributes: Elfwood member, Art Q: (QA87) A: Attributes:

do you have a Elfwood member I only have Number Art that might be good enough to put up, and Art are only half done Elfwood member, Art

(QA88) A:

do you have a Elfwood member No, I suck at Art

Q: (QA89) A: A: Attributes:

do you have an Elfwood member nope, I Art a lot, but I dont have a Scanners nope, I Art a lot, but I don't have a Scanners

Q:

Attributes: Elfwood member, Art

Elfwood member, Art, Scanners

(QA90) A:

you are on Elfwood member Yeah, I've got a few Stories and a Stories up

Q: (QA91) A: Attributes:

do you have an Elfwood member I tried to make an Elfwood member, but I don't know how to Picture upload

Q:

Attributes: Elfwood member, Writing technical

Elfwood member, Picture upload

APPENDIX A. GRAMMAR SPECIFICATION

164 Q: (QA92) A: Attributes:

do you have an Elfwood member no but I plan on getting an Elfwood member when I get a

Q: (QA93) Q: A: Attributes:

where's your gallery...on Elfwood member where's your gallery, on Elfwood member Fantasy Gallery Number

Q: (QA94) Q: A: Attributes:

ehere's your gallery where's your gallery I am in gallery Number

Scanners Elfwood member, Scanners

Link to art

Link to art

Q:

Know the gallery

Q:

which gallery are you in...do you know the number I found my gallery Number

(QA95) A:

Link to art Attributes: Link to art

(QA96) A:

Attributes: Link to art

Q: (QA97) Q: A: Attributes: Q:

(QA98) A:

whats your site url what's your site url Link to art Link to art

Do you have pictures up somewhere

Link to art Attributes: Link to art

Q: (QA99) Q: A: Attributes: Q:

(QA100) A:

Do you have a webpage up here Do you have a web page up here Link to art Link to art

you have an Elfwood member it's Link to art

Attributes: Link to art Q:

(QA101) A:

do you have a Elfwood member

Link to art Attributes: Link to art

Q: A: (QA102) A: Attributes:

do you have an Elfwood member I'm in Gallery Number I think....or Number, it's Link to art I'm in Gallery Number, I think, or Number, it's Link to art Link to art

A.1. GRAMMAR RULES Q:

Do you have a Elfwood member

Q:

are you an Elfwood member Gallery Number

(QA103) A:

Link to art Attributes: Link to art

(QA104) A:

Attributes: Link to art

(QA105) A:

are you an Elfwood member Yeah, my site's at Link to art

Q: (QA106) A: A: Attributes:

are you it's Number, loth it's Number, lothlorien

Q:

Attributes: Link to art

Link to art

Q:

can I see some of your work

Q:

if I could get the address to your Art

Q:

What's your URL my URL for Elfwood member is Link to art

(QA107) A:

Link to art Attributes: Link to art

(QA108) A:

Link to art Attributes: Link to art

(QA109) A:

Attributes: Link to art

(QA110) A:

where is your gallery Elfwood member gallery Number Name

Q: (QA111) A: A: Attributes:

where's your Elfwood member gallery LNumber ZNumber Name Lothlorien Number, Zone Number, Name

Q: (QA112) A: A: Attributes:

do you have a Elfwood member gallery Number..Name gallery Number, Name

Q: (QA113) A: A: Attributes:

do you have a Elfwood member yes gallery Number artist: Name yes, gallery Number, artist: Name

Q:

Attributes: Link to art, Name

Q:

(QA114) A:

Link to art, Name

Link to art, Name

Link to art, Name

are you yes, gallery Number, i think, Name

Attributes: Link to art, Name

165

APPENDIX A. GRAMMAR SPECIFICATION

166 Q:

and yours

Q:

library i'm in Number

(QA115) A:

Link to stories Attributes: Link to stories

(QA116) A:

Attributes: Link to stories Q:

Where is your library page

(QA118) A:

Q:

what gallery is your work in I'm number Number

Q: A: (QA119) A: Attributes:

may I have the link Elfwood member The URL is- -Link to stories The URL is Link to stories

(QA117) A:

Link to stories Attributes: Link to stories

Attributes: Link to stories

Link to stories

Q:

can I have the link to your area of Elfwood member

Q:

you are on Elfwood member

Q:

Do you have Writing online

Q:

how long have you been Art I've been Art since I was little

(QA120) A:

Link to stories Attributes: Link to stories

(QA121) A:

Link to stories Attributes: Link to stories

(QA122) A:

Link to stories Attributes: Link to stories

(QA123) A:

Attributes: Art Q: A:

(QA124) A:

how long have you been Art since i was about Number, but i stopped Art when i'v got a computer and i just started again since i was about Number, but i stopped Art when i got a computer and i just started again

Attributes: Art

(QA125) A:

are you a Skill level artist no

Q: (QA126) A: A: Attributes:

did you take the Art yes i have yes i did

Q:

Attributes: Art

Art

A.1. GRAMMAR RULES (QA127) A:

have you taken any Art I've really only taken Number Art

Q: Q: (QA128) A: A: Attributes:

you got any Pictures uploaded somewhere else have you got any Pictures uploaded somewhere else Nope No

Q: A: (QA129) A: Attributes:

what brand Watercolour....don't know how to use Watercolour Watercolour, don't know how to use Watercolour

Q:

Attributes: Art

Art

Watercolour

Q:

with what medium

Q:

what medium are you using i use Pencil mostly

(QA130) A:

Pencil Attributes: Pencil

(QA131) A:

Attributes: Pencil Q:

(QA132) A:

Pencil

Yes

Attributes: Pencil Q:

(QA133) A:

Pencil

mostly

Attributes: Pencil Q:

do you use a Pencil or a Ink

Q:

you drew with a Pencil or Ink

(QA134) A:

Pencil Attributes: Pencil

(QA135) A:

Pencil Attributes: Pencil

Q: Q: (QA136) A: A: Attributes: Q:

(QA137) A:

how do you want to draw Nature...Pencil how do you want to draw Nature, Pencil Yep Yes Pencil

What kind of medium are you using I pretty much use Pencil/Coloured pencil

Attributes: Pencil, Coloured pencil

167

APPENDIX A. GRAMMAR SPECIFICATION

168 Q: Q: (QA138) A: Attributes:

how do you want to draw Nature...Pencil how do you want to draw Nature, Pencil Coloured pencil added later

Q: (QA139) A: Attributes:

have you tried other media Pastel, Acrylics and I Digital my Anime/Manga in Paintshop

Pencil, Coloured pencil

Pro Pastel, Acrylics, Digital, Anime/Manga, Paintshop Pro

(QA140) A:

you do Digital A little

Q: (QA141) A: A: Attributes:

do you know about Digital Yeah Yes

Q:

Attributes: Digital

Digital

Q:

Have you got one of those Graphics tablets, or do you use a

Q:

What medium

(QA144) A:

Q:

Do you have any photo-editing program I have Adobe Photoshop

Q: (QA145) Q: A: Attributes:

you got Adobe Photoshop have you got Adobe Photoshop No

Q: (QA146) Q: A: Attributes:

you got Adobe Photoshop have you got Adobe Photoshop I have Paintshop Pro

Q: (QA147) A: A: Attributes:

what version I've got Paintshop ProNumber I've got Paintshop Pro Number

Q: (QA148) Q: A: Attributes:

what Paintshop Pro you got what Paintshop Pro have you got I have Paintshop Pro Number and Number

Digital Digital Attributes: Digital

(QA142) A: (QA143) A:

Adobe Photoshop Attributes: Adobe Photoshop Attributes: Adobe Photoshop

Adobe Photoshop

Paintshop Pro

Paintshop Pro

Paintshop Pro

A.1. GRAMMAR RULES (QA149) A:

you want help with Adobe Photoshop No, Paintshop Pro

Q: (QA150) A: A: Attributes:

do you have a Graphics tablets Nope No

Q:

169

Attributes: Paintshop Pro

Q:

(QA151) A:

Graphics tablets

What type of pictures do you usually draw

Art objects Attributes: Art objects

Q: A: (QA152) A: Attributes:

Are you a Elfwood member or Elfwood member hopeful Most of my art is Fantasy, but I do alot of Sci-Fi also Most of my art is Fantasy, but I do a lot of Sci-Fi also

Q: (QA153) A: A: Attributes:

You with the Art objects yep, im the Art objects Gender yes, i'm the Art objects Gender

Art objects

Art objects, Gender

(QA154) A:

Do you have pictures up somewhere I have some of Title and the Title characters

(QA155) A:

Q:

what kind of pictures do you typically draw usually Humans of Humans

Q: (QA156) Q: A: Attributes:

what problem are you haveing speci cally what problem are you having speci cally the Humans

Q: (QA157) Q: A: Attributes:

with the Humans, what kinda problems are you having with the Humans, what kind of problems are you having the Humans does not look remotely like a Humans

Q:

Attributes: Art objects, Humans

Attributes: Humans

Humans

Humans

(QA158) A:

do you put Humans on the Humans I do do Humans

Q: A: (QA159) A: Attributes:

Humans,Humans,Art objects Humans, Humans, Art objects Humans, Art objects

Q:

Attributes: Humans

What exactly are you trying to draw

APPENDIX A. GRAMMAR SPECIFICATION

170

(QA160)

Q: Q: A: A:

what do you use to draw what do you usually draw Humans, Humans, some Anime/Manga- Animals- Art styles and Animals Humans, Humans, some Anime/Manga, Animals, Art styles and

Animals Attributes: Humans, Anime/Manga, Animals, Art styles Q:

(QA161) A:

what do you like to draw Animals, Animals

Attributes: Animals Q:

(QA162) A:

what kind of Animals do you like best

Animals Attributes: Animals

Q: (QA163) Q: A: Attributes: Q:

(QA164) A:

what problem are you haveing speci cally what problem are you having speci cally Nature like the Nature or the Nature or the Nature Nature

have you made Anime/Manga drawings i have made Anime/Manga drawings

Attributes: Anime/Manga Q:

(QA165) A:

do you want Anime/Manga or Realism

Anime/Manga Attributes: Anime/Manga

Q: (QA166) Q: A: Attributes:

the Anime/Manga or the one thats a bit more Realism the Anime/Manga or the one that's a bit more Realism the more Realism version is what I'm aiming for

Q: (QA167) A: Attributes:

what do you like to draw I like drawing Anime/Manga Humans and Humans, or just normal Anime/Manga Humans

Q:

(QA168) A:

Anime/Manga

Anime/Manga, Humans

what sort of things do you usually like to draw I like to draw Anime/Manga and sometimes Realism

Attributes: Anime/Manga, Realism Q:

(QA169) A:

do you know about Art techniques were doing Art techniques in art class

Attributes: Art techniques Q:

(QA170) A:

do you use Art techniques or do you draw Art techniques

Art techniques Attributes: Art techniques

A.1. GRAMMAR RULES Q:

(QA171) A:

171

what sort of system do you use when drawing Humans I mainly use Art techniques

Attributes: Art techniques

(QA172) A:

Do you Art techniques everything usually

Q: (QA173) A: A: Attributes:

Do you use Perspective when you draw not usually i don't really like Perspective too well not usually, i don't really like Perspective too well

Q:

Attributes: Art techniques

Perspective

(QA174) A:

how long have you been interested in Writing as long as i can remember

(QA175) A:

Q:

how long have you been interested in Writing ever since i rst put ink to paper in words

Q: A: (QA176) A: Attributes:

how long have you been interested in Writing Writing was always my favourite part in english Writing was always my favorite part in english Writing

Q: A: (QA177) A: Attributes:

do you have an idea I began as Writing... so a Stories is no problem I began as a Writing... so a Stories is no problem

Q: (QA178) A: A: Attributes:

what time period are you writing in kinda Fantasy kind of Fantasy

Q: (QA179) A: Attributes:

What type of stories do you normally write I write Fantasy, Horror, and some random Stories...some Sto-

Q:

Attributes: Writing

Attributes: Writing

Q: A:

(QA180) A:

Writing, Writing technical

Fantasy

ries Fantasy, Horror, Writing technical

What type of stories do you normally write most of my Fantasy writing will tend to invlove Characters or Humans/Humans most of my Fantasy writing will tend to involve Characters or Humans/Humans

Attributes: Fantasy, Characters Q:

(QA181) A:

Is a Stories uploaded You'll nd a Stories at Link to stories

Attributes: Writing technical, Link to stories

APPENDIX A. GRAMMAR SPECIFICATION

172 (QA182) A:

May I ask what genre your Stories is Sci-Fi/Fantasy

Q: (QA183) A: A: Attributes:

what time period are you writing in w/Culture and Culture in uences, mainly Culture with Culture and Culture in uences, mainly Culture

Q:

Attributes: Writing technical, Sci-Fi, Fantasy

Setting

(QA184) A:

are you new to Elfwood member not very

Q: (QA185) A: A: Attributes:

Do you use Elfwood member often Yep Yes

Q:

Attributes: Elfwood usage

Q:

(QA186) A:

Elfwood usage

do you read a lot of Stories at Elfwood member Yes

Attributes: Stories

(QA187) A:

Have you been to the Intranet Just to Picture upload

Q: (QA188) A: A: Attributes:

what's Tour creation called Title, (Stories) I think is what I called my Tour creation Title (Stories) I think is what I called my Tour creation

Q:

Attributes: Intranet, Picture upload

Q:

(QA189) A:

Tour creation

are you on Computer or on Computer

Computer Attributes: Computer

Q: (QA190) A: A: Attributes:

could you mail Art to me ive never tryed i've never tried

Q: (QA191) A: A: Attributes:

have you ever played a Internet Extended yup, I got to a Internet Extended regularly yes, I go to a Internet Extended regularly

Q:

(QA192) A:

Internet

Internet, Extended

have you ever played a Internet Extended Number, actually

Attributes: Internet, Extended

A.1. GRAMMAR RULES Q:

(QA193) A:

173

have you ever played a Internet Extended Title and Title

Attributes: Internet, Extended

Q:

are you on Computer or on Computer

Q:

What kind of Stories do you like to read I read Stories of both Fantasy and Sci-Fi

(QA194) A:

MS Windows Number Attributes: MS Windows

(QA195) A:

Attributes: Extended

(QA196) A:

ever read Title yes

Q: (QA197) A: A: Attributes:

do you Extended ya, sure yes, sure

Q: (QA198) A: A: Attributes:

how much do you work Some Time every Time..In Occupation to Some Time every Time, in Occupation too

Q:

Attributes: Extended

Rej-Soln

Extended

Extended, Occupation

(RS1) Rej:

You could try the Internet, Elfwood data i just know Country Internet

Soln:

I'll listen to a certain type of Extended Extended doesn't really make me draw

Soln:

Attributes: Country, Internet

(RS2) Rej:

Attributes: Art

(RS3) Rej:

you might want to try Watercolour I use Watercolour for my Art techniques

Soln: (RS4) Rej: Rej: Attributes:

reduce the size of the Humans with a Digital I don't have much experience inDigital I don't have much experience in Digital

Soln:

Attributes: Watercolour, Art techniques

Soln: Rej:

(RS5) Rej:

Digital

a Graphics tablets would suit you very nicely I do have a Graphics tablets but, but strangly I prefer a Digital a Digital works better for me I do have a Graphics tablets, but strangely I prefer a Digital, a Digital works better for me

Attributes: Graphics tablets, Digital

APPENDIX A. GRAMMAR SPECIFICATION

174 Soln: (RS6) Rej: Attributes:

You could try some Art objects I did try some Art objects, but all of my Title pictures didn't look right

Soln: (RS7) Rej: Attributes:

try to draw a Characters I tried to draw a Characters, and to draw a Characters didn't work

Art objects

Humans

(RS8) Rej:

you can try to break the Humans into simple form tried break the Humans into simple form

Soln:

you can try to break the Humans into simple form but the Humans look ugly afterwards

Soln:

Attributes: Humans

(RS9) Rej:

Attributes: Humans Soln: Rej:

(RS10) Rej:

Do you think either of those methods would work for you I have tried the Art techniques thing but htey still turn out googly eyed I have tried the Art techniques thing, but they still turn out googly eyed

Attributes: Art techniques Soln:

(RS11) Rej:

you select a Perspective did select a Perspective

Attributes: Perspective Soln:

(RS12) Rej:

draw a Plot Plot I know

Attributes: Plot Soln: Rej:

(RS13) Rej:

do you know how to get to User functions I tried User functions but there were to many Animals Pictures even when I tried to narrow User functions down I tried User functions but there were too many Animals Pictures even when I tried to narrow User functions down

Attributes: User functions Soln: (RS14) Rej: Rej: Attributes:

have you tried User functions Ihave tried to User functions but it will be so many ansvers I have tried to User functions but it will be so many answers

Soln: (RS15) Rej: Attributes:

have you tried the Tour creation I tried some of the Tour creation but didn't nd any that went through Buildings

User functions

User functions

A.1. GRAMMAR RULES

175

Soln: (RS16) Rej: Rej: Attributes:

there are some FARP (usage) ive read FARP (usage) i've read FARP (usage)

Soln: (RS17) Rej: Rej: Attributes:

you can always visit the ocial Internet I can't go past the Internet, cuz I'm using a Computer I can't go past the Internet, because I'm using a Computer

Soln:

(RS18) Rej:

FARP (usage)

Computer

do you have any friends who have a Scanners no but we have a Scanners at Occupation

Attributes: Scanners

Soln:

(RS19) Rej:

just buy a Stories I can't buy a Stories

Attributes: Extended

Soln:

(RS20) Rej:

try Extended some Fantasy Stories I do Extended some Fantasy Stories

Attributes: Extended

Soln:

(RS21) Rej:

You could try some Art objects I don't like Extended

Attributes: Extended

Soln:

(RS22) Rej:

i'm pretty sure that there are a few Animals Pictures in Elfwood member There are more then a few, but i don't like Extended There are more than a few, but i don't like Extended

Rej: Attributes: Extended

Pres-Com Pres:

(PC1) Com:

Link to art

Your work is Skill level

Attributes: Art

Pres: (PC2) Pres: Com: Attributes:

you can see my gallery, its number Number you can see my gallery, it's number Number ooo...Watercolour Watercolour

Pres:

Pictures Digital artist, I see Attributes: Digital

(PC3) Com: Pres:

(PC4) Com:

I am in gallery Number you're good with the Digital and great with your Pencil

Attributes: Digital, Pencil

APPENDIX A. GRAMMAR SPECIFICATION

176 Pres: Com: (PC5) Com: Com: Attributes: Pres:

(PC6) Com:

it's Link to art the Adobe Photoshop one os really good colored the Adobe Photoshop one is really well colored the Adobe Photoshop one is really well coloured Adobe Photoshop Personal data

Wow, nice, good Humans

Attributes: Humans Pres:

(PC7) Com:

it's Link to art you draw too short Humans

Attributes: Humans Pres:

(PC8) Com:

tell me about my Art Humans're way too short

Attributes: Humans Pres:

(PC9) Com:

tell me about my Art you should draw longer Humans

Attributes: Humans Pres: Com:

(PC10) Com:

Com:

it's Link to art you need practise in the basic stuuf... Humans Perspective, Humans Perspective you need practise in the basic stu: Humans Perspective, Humans Perspective you need practice in the basic stu: Humans Perspective,

Humans Perspective Attributes: Humans, Perspective

Pres: (PC11) Com: Attributes:

tell me about my Art the Humans on the Characters Pictures is pretty good, the Perspective of the Humans are ok Humans, Perspective

Pres: Pictures Com: ah ! a Animals (PC12) Attributes: Animals Pres: (PC13) Pres: Com: Attributes: Pres:

(PC14) Com:

Galler Number Gallery Number you're deep into Animals Animals

You'll nd a Stories at Link to stories Stories is a Stories from a Stories

Attributes: Writing technical

A.2. VOCABULARY LIST

177

A.2 Vocabulary List Personal data archway [email protected],

[email protected], [email protected], e-mail address, e-mail/hotmail, e-mails, [email protected], email/hotmail, emails, home page, homepage, hotmail, http://beta.insolwwb.net/lheyl/rpg/, http://falcon.cc.ukans.edu/qandrews/, http://members.spree.com/entertainment/blackcypress/tavern/char/ draklor.htm, http://www.geocities.com/arae51/keep/7551, http://www.geocities.com/area51/keep/7551, http://www.geocities.com/Area51/Rampart/3293/dandeplu.html, mail, [email protected], organisation, organization, [email protected], [email protected], www.eccentricman.co.uk

Age adult Gender girl, wench Country english, English, Florida, france, french, French, german, Ger-

man, germany, Israel, sweden, Sweden, the UK, utah, Yankee

Occupation grade, High School, public school, published, school, student, trade

178

APPENDIX A. GRAMMAR SPECIFICATION

Name Adriana, Ally Hartley, Alon, Alycia Lampshire, Angela Theodora

Massop, Beldarr, beth, bisson, Centauress, Chelsea, Cheng Hui Ling, Christine Beyaert, Devlin Faine, DN668, eccentricman, Eiram Haq, Elena, Elessil, Emma, Hanna, Irene, jen, Jessica, jessica mann, Johannes Liljenberg, Jordan, JWC, Kerry Nash, Kier, Laura Church, lawrencek, Lewis, malissa L. Hawkins, Marcus, marcus gronbech, Marisa, Matt, mdel, `mdel', michael alan hibbert, Mitchell Hunter, Pam Wol, Qiana, Rikke, Sarah, Srishti, Susanna G. Mead, Taylor Foxx, TeknoKat, Tim, Tolkien, Ven, W. Irene Thanh, Wade

Conversation style post, things that you have to look up Elfwood data #lothlorien, Ass.'s, assistant, Assistants, elfwodd assistant, elfwood assistant, Wyverns list

Elfwood member e wood member, E wood member, elfwood, Elfwood,

elfwood address, Elfwood artist/writer, elfwood gallery, Elfwood gallery, elfwood member, Elfwood member, elfwood page, elfwood space, elfwood site, Elfwood writer, EW artist, galler y in Elfwood, gallerey on elfwood, gallery, gallery at elfwood, gallery in elfwood, gallery on elfwood, gallery on Elfwood, gallery on EW, gallery or library on elfwood, gallery or shelf on Elfwood, loth, loth or zone galelry, loth or zone gallery, loth/zone page, lothlorien, lothlorien or zone gallery, Lothlorien, lothlorien/zone page, member, member of elfwood, member of Elfwood, member of lothlorien and the library, member of the sci gallery, member of the sci- gallery, member of the Wyvern's Library, member on elfwood, shelf, shelf or gallery, site on elfwood, story gallery, the Library, the woods, wyvern, Wyvern, Wyvern's, Wyverns, zone, Zone, zone 47, Zone gallery

Link to art artists/crys/crys.html,

elfwood.lysator.liu.se/lothlorien/artists/exner/exner.html, elfwood.lysatorblabla... lothlorien/artists/exner/exner.html, http://elfwood.lysator.liu.se/lothlorien/artists/aviv/aviv.html, http://elfwood.lysator.liu.se/lothlorien/artists/cardi/cardi.html, http://elfwood.lysator.liu.se/lothlorien/artists/dima/dima.html, http://elfwood.lysator.liu.se/lothlorien/artists/gett/gett.html, http://elfwood.lysator.liu.se/lothlorien/artists/hawkridge/hawkridge.html, http://elfwood.lysator.liu.se/lothlorien/artists/jessatc/ lotsofrandomjunk.jpg.html, http://elfwood.lysator.liu.se/lothlorien/artists/johnroden/johnroden.html, http://elfwood.lysator.liu.se/lothlorien/artists/lawrencek/lawrencek.html,

A.2. VOCABULARY LIST

179

http://elfwood.lysator.liu.se/lothlorien/artists/lozanova/lozanova.html, http://elfwood.lysator.liu.se/lothlorien/artists/knash/knash.html, http://elfwood.lysator.liu.se/lothlorien/artists/marisa/marisa.html, http://elfwood.lysator.liu.se/lothlorien/artists/mdel/mdel.html, http://elfwood.lysator.liu.se/lothlorien/artists/ruben/aviendha.jpg.html, http://elfwood.lysator.liu.se/lothlorien/artists/wol/wol.html, http://elfwood.lysator.liu.se/lothlorien/artists/zepf2/zepf2.html, lothlorien/artists/bunnysb/bunnysb.html

Link to stories http://elfwood.lysator.liu.se/library/writers/aly/

aly.html, http://elfwood.lysator.liu.se/library/writers/aly/gemalstale.htm.html, http://elfwood.lysator.liu.se/library/writers/geraldotto/geraldotto.html http://elfwood.lysator.liu.se/library/writers/leander/leander.html, http://elfwood.lysator.liu.se/library/writers/leonard/leonard.html, http://elfwood.lysator.liu.se/library/writers/leonard/leonard,html, http://elfwood.lysator.liu.se/library/writers/waelinh/waelinh.html, http://elfwood.lysator.liu.se/library/writers/zak2/zak2.html

Art art, art classes, art course, art work, artist, artistic talent, draw,

*draw*, draw a line, drawing, drawing skill, drawings, ne manipulation skills, julie dillon's tutorials, pricing, requests

Art media paper, Printer paper, sculpters, sculptures Wet magic pens, paint, painting Ink ink, pen, traditional Chineese inks, traditional Chinese inks Oil paint oil Watercolour dewerts, gouache, watercolor, watercolors, watercolour, wa-

tercolours

Acrylics acrylic painting, acrylics Dry Pencil 2B, 4B, F, HB, mechanical pencil, pencil, Pencil, penciling, pencils

180

APPENDIX A. GRAMMAR SPECIFICATION

Coloured pencil colored pencil, colored pencils, coloured pencil, coloured pencils, pencil crayon

Charcoal Conte Pastel pastels Digital airbrush, cg, CG, compu painting, computer color, computer col-

our, computer graphics, computer paint/color program, computer paint/colour program, computer painting, compy color, compy colour, Corel Draw, layer, layers, magic wand, mouse, photo manipulation, Photodeluxe, photomanipulation, program like photoshop, Photopaint

Adobe Photoshop Adobe Photoshop, photoshop, Photoshop MetaCreations Painter Paintshop Pro paint shop, paintshop, Paintshop Pro, PSP Graphics tablets digital drawing tablet, tablet 3D programs 3d studio Art objects background, backgrounds, deamons, demons, fan art, fanart,

mask, masks, muscles, musculature, really frantically gross things, skulls, swords, unpleasant things, unpleasent things

Humans anatomy, bodies, body, character drawings, chest, chests, cloth-

ing, el n, el n mages, el n magicians, el n wizards, el sh, elven mages, elven magicians, elven wizards, elves, elvish, eyelashes, eyes, face, faces, facial studies, Faeries, fairies, faries, feet, females, nger, ngers, girls, guys, hair, hands, human, humans, human-types, legs, modern dress, nose, nude female gure, people, portraits, waist, waists, Vampires, wardrobe, women, Women angels

A.2. VOCABULARY LIST

181

Animals animals, big cats, chimeras, chinese dragon, chinese dragons, dragon, Dragon, dragon heads, dragons, four legged things, furries, horse, horses, insect, insectioid, insectoid, medieval classic dragon, satyr, tail, TeknoKats, trollocs, unicorn, wings, Wings Buildings buildings, castles Nature bolt of light, dust, \ ery", re, rey, ry, ames, gust of wind, leaves, magic actions, texture, trees Art styles comic, comics, cartoon, cartoonish, cartoons, still lifes, still lives

Realism life, real, \real", realistic Anime/Manga anima/magna, anime, Anime, anime inspired, anime/manga, anime-inspired, anime-style, manga/anime, small and cute style

Impressionist Art nouveau Art techniques clean up my pics, clean up my pictures, coloring, colour-

ing, complementary colors, complementary colours, complimentary colors, cross hatch, cross hatching, looking in the mirror at me and drawing, out of my head, out of your head, outline, pictures from real life, render, renderings

Perspective angle, bubbles, depth, at, looking at me, perspective, per-

spectives, porportions, pro les, proportion, proprotion, proportions, shade, shaded, shading, singel direction light is coming from, single direction light is coming from, source of light

Sketching doodling, sketch, sketches, `sketches', sketching Detail drawing detail, up close Writing publishers, write, writer, writer's block, writers block, writing,

writings

182

APPENDIX A. GRAMMAR SPECIFICATION

Writing styles Humour Serious writing articles Fantasy epic fantasy, fanasy, fantasy, Fantasy, high fantasy, \high fantasy", medieval

Sci-Fi Sci Fi, sci- , Sci-Fi, Science Fiction, ScienceFiction, sci , SciFi, si Horror horror Writing technical keeping things out of the story Grammar Oxford Advanced Learner's Dictionary Characters character from a dierent time line, characters, evil wizard, gay men, homosexual, one of my rp chars, one of my roleplaying characters, strong feminine characters, treebeard

Setting gods, pantheons, race Plot epic, linear writing, plot tree, story line Point of view Elfwood usage Pictures black5.jpg,

cover art, http://www.warclan.com/fronzel/giniha.jpg, http://www.warclan.com/fronzel/lionking2.jpg, jake.gif, pic, pics, picture, pictures, picutr,

A.2. VOCABULARY LIST

183

pisture, wizard.jpg

Stories a longer piece of work, a longer piece of wprk, book, books, cycle,

novel, novels, poem, poetry, short stories, stories, story, stroies, strories, trilogy

Member functions Intranet intranet Tour creation tour, tours Picture upload upload a picture, upload a pictures, upload pictures FARP (creation) User functions elfwood search, search, searching, searhing, usual searches, \usual searches"

Text search Attribute search FARP (usage) everything on farp, things on FARP FantasyHoo Fantasyhoo Computer computer, macs, Mac, MAc, PC, programmer, public computer Internet cable modem, Cchat, chat, Explorer, HTML, html programming, internet, IRC channels, IRC servers, message board, moos, MUSHes, Netscape Communicator, netscape, online, web page, webpage Scanners scan, scaner, scanner, Scanner, scanning MS Windows MS win, MS....*snear*...win, MS....*sneer*...win, Windows

APPENDIX A. GRAMMAR SPECIFICATION

184

Linux Unix Extended board, cards, copying other people's ideas, gemetric drawing,

geometric drawing, geometry, music, player, read, reading, role playing, roleplay, role-player, role-playing, role-playing game, roleplay, roleplayer, rp, RP games, RPer, rpg, RPG, RPG games, TV, using elfwood artist's pictures as models, using elfwood artists' pictures as models

Art category Art creation, Art navigation, Art techniques Literature category Story creation, Story inspiration Colour bold bright colors, bold bright colours, color, colour, white Culture Asian, Caucasian Number one, 2, two, Two, 3, three, four, 5.0, 5.7, 6, six, 7, 8th, 9th, 11th, 12, a dozen, 13, 14, 15, 16, 19, 20-odd, 22, 88, 89, 95, 98, 103, 113, 161, 168, 171, 199, 221, 256, 287, 294, 300, 305, 318, 329, 337, 346, 351, 404, zillions

Place Guernsey, my mom's oce, Soest Skill level advanced, begginer, beginner, professional level Time 3 or 4 in the morning, day, hours, night, years, yearss, yrs Title

1

Ad&d, Aliens, \Aliens", Amber Chronicles, dbz, DC Heroes, Dragon Ball Z, Gemal's Tale, `Gemal's Tale', GURPS, lord of the rings, Lord of the Rings, lords of the rings, Mylanders, Once upon a Time, the Court of Worlds, the Spider's Place, Wheel of Time 1 A \Title" in this domain would be the title of a book or an Elfwood guided tour, not

the rank or position of a person.

A.2. VOCABULARY LIST

185

Coreferring any of it, her, his, ideas, inspiration, it, its, many, mimics, mine, one, ones, originals, some, stu, such a prog, such a program, that, the rst two, their, them, there, they, thing, things, this, this eld, this site, those, version