Intent feature discovery using Q&A corpus and web data

Intent Feature Discovery using Q&A Corpus and Web Data Soungwoong Yoon, Adam Jatowt and Katsumi Tanaka Graduate School of Informatics, Kyoto University Yoshida Honmachi, Sakyo, Kyoto 606-8501, Japan

{yoon, adam, tanaka} @ dl.kuis.kyoto-u.ac.jp ABSTRACT

only the top ranked results for finding appropriate documents [1].

User intent in Web search environment is defined as user’s information need, and believed to be found by analyzing past data such as queries, click histories and user profiles. However, users may have different intents even in the same queries. In this paper, we attempt to discover the characteristics of intent through finding its features. Our assumption is that if a user expresses the query which clearly points out to certain intent, s/he can reach an intended Web page using that query. We conceptualize this functionality of intent features using intent evolution procedure, called multiple intent model. We collect candidate intent features using Web Q&A corpus analysis, and suggest the automated judgment method using search engine indexes powered by Click Chain Model to demonstrate the adaptability of candidate intent features. Experimental results show that intent features can be extracted efficiently and provide evidences toward intent discovery without human supervision.

We can simply define users’ intent as their information needs in Web search environment, which means that discovering user intents on the Web will be a premise to satisfy their needs. Until now researchers characterized intent as specified categories or clusters which were found by analyzing past user data [3,10,12]. But these aggregation or distillation methods can provide ways to find general intent-driven categories or clusters, rather than detect the actual user intents. In addition, the past data is useful enough to find statistically dominant intent of a query, but not sufficient enough to cover the variety of intents.1

Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval – Information filtering, Query formulation, Search Process

General Terms Algorithms, Experimentation.

For information providers, as well as the users themselves, it is difficult to express intent precisely because they may not possess sufficient knowledge to ‘imagine’ and ‘conceptualize’ their needs and, moreover, the intent may change following various conditions such as querying time, search environment and acquired knowledge in the previous search sessions [18]. When users lack the knowledge or contextual awareness to formulate queries or navigate complex information spaces, the search task requires browsing and exploration [16]. In this paper, we attempt to discover the characteristics of intent through finding its features. We assume that users can reach their intended Web pages if they express the query which clearly points out to required intent.

Keywords User Intent Features, Query Intent, Web Q&A Corpus, Click Chain Model.

1. INTRODUCTION World Wide Web is the biggest repository of information and knowledge. However, the Web is far from being a well arranged ‘treasury’. Numerous attempts have been proposed to arrange Web data by certain standards such as ontological hierarchy, questions and its answers, or annotations and bookmarks. However, users send simple queries to search engines in order to find appropriate Web contents, the retrieved results still have some defects and may be ineffective for finding required information. Moreover, it has been observed that users often scan

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Figure 1. Concept of Intent Expressions and Features Figure 1 demonstrates the concept of finding intent features. User can choose his/her precise intents behind a simple query composed of words and phrases. Even though there may be numerous expressions reflecting limited number of intents, we can still find possible expressions related to the query. We call these words and phrases as intent expressions, and features distilled from an expression can support the semantics which represents 1

For example, there are about 29.6% (random sample) – 46.5% (entire data) non-clicks in AOL 500K User Session Collection (http://www.gregsadetsky.com/aol-data/), which means that one should analyze users’ clicks as well as non-clicks for estimating user satisfaction with search results.

the intent. For example, the query ‘kyoto travel’ has possible intent expressions such as ‘what is the cheapest way to go to Kyoto?’, ‘good hotel in Kyoto,’ and many others. Using these expressions, we can distill explicit features which should be useful to represent possible intents such as ‘cheap(est) way,’ ‘Kyoto’ or ‘good hotel’, and implicit features which support intent such as ‘travel.’ Our objective is finding these features and showing their usefulness in relation to concrete intent. Although this concept is comparably easy to be recognized for a user, it is difficult for search engines, because the machine cannot measure the degree of intent. In the previous example, the given two expressions clearly support the intent ‘(how to) travel to Kyoto,’ but simultaneously they can be divided into two different intents – ‘flight ticket to Kyoto’ and ‘hotel in Kyoto’ – even thought the query ‘kyoto travel’ is not changed. We try to find ways to distill these useful intents using their features by harnessing knowledge accumulated on the Web without human supervision. Our method to discover intent features has two stages. To find candidate intent features, Web Q&A corpus such as Yahoo! Answers2 (YA) is used to collect candidate features because the questions in Web Q&A corpus are good expressions to reveal questioners’ intents. Next, we utilize automated comparison method between search results of original queries and those of queries concatenated with features to measure the efficiency of features distilled from intent expressions. For these comparisons, we propose the indictor function using click probability of Click Chain Model (CCM) [9], which simulates user’s search behavior. CCM assumes that users have the same intent within the same query and, then, the relevancies of search results by the query are random. However, in our work, we assume that users may have different intents within the same query, and concatenation of the query and a feature represents a possible intent of the query. Then search results’ relevancies of these concatenated terms are supposed to be not random, but increase in higher ranks. With discriminative aggregation of feature effects without human supervision, we can distill useful intent features to show user efficient intents. These features will serve as a good guidance to find out intent itself when user lacks background knowledge related to intended information, or could be used as criteria of recommendation to facilitate user’s choice of intent. The paper is organized as follows. Section 2 shows related work. We describe our methods to find intent features and their efficiency in Section 3. Experimental results and discussions are shown in Section 4. Finally, we conclude the paper in the last section and discuss our future work.

2. RELATED RESEARCH 2.1 Intent Discovery Research on search intent discovery originated from the analysis of click-through data and query-intent categorization. Following the well-known query classification first proposed by Broder [3], Jansen et al. [10] stated that user intent can be categorized into three general intent classes: navigational, transactional and informational. The characteristics of user intent have been conventionally defined by analyzing click-through data [3,10,12].

2

http://answers.yahoo.com

Using large amounts of data containing evidence of user searchrelated activities made it possible to not only understand user needs, but also to depict user behavior in browsing Web search results [8] or support non-informational search intent on the Web [13]. However, the usefulness of these categorizations is limited by the data sets used and the efficiency of post-processing. There is a risk of the over-generalization being reflected in mismatches in classification between automatic and manual categorization. This is because the above researches were based on their own rigid classification schemes, and biased by the data sets they used. Furthermore, the previously proposed methods often have failed to represent the actual user intent as the scope of possible intents may simply be too large and too heterogeneous to be accurately reflected in any fixed taxonomy. Moreover, one should realize that the information needs of Web users are constantly changing and so does the Web itself. Diversifying search results [6] and exploratory search [11,16] are attempts for finding unknown factors such as intra-clustering or interface problems, but still they lack means for discovering actual user intent. Automatic intent feature discovery has been tried using its matches with search result information [18]. However, in that case the relevancy had to be judged manually and semantic portions of intent features have not been sufficiently considered. In this work, we try to find the intent itself to overcome these broad-categorization problems. Both user-specific and statistically dominant viewpoints as well as direct intent expression can help users to choose their intent within possible suggestions, which can cover up their laziness when finding goals on the Web.

2.2 Query Expansion and Term Suggestion Query expansion is basic method to extend the semantic span of original query in order to find relevant results. Statistic analysis [5] and semantic, user behavior based approaches [7,17] are used to expand query, and are naturally connected with query refinement and reformulation. In this area, researchers treat intent as user feedback on similar patterns on the Web [2]. This approach is useful when the query is semantically concrete such as bidterm suggestions [4]. Nevertheless, these methods have no viewpoint of intent, even though we can use same methods such as relying on existences of terms or its patterns. They expanded terms concerned with query to collect more relevant results, but had same criteria to distill relevant results as original query. We diversify query’s relevancies concerned with its features extracted.

3. DISCOVER INTENT FEATURES For discovering intent features, we send the query to search engine to retrieve relevant search results, and, at the same time, forward it to Web Q&A corpus in order to collect candidate intent features. Extracted features are then sent to search engine again concatenated together with query. We return real intent features and intended top-k search results of original query using comparison of search result sets by query and those of query and intent features. Figure 2 shows the overall configuration of our system.

concept

User action U1 U2 U3 I I P I E P E E I P E

Intent evolution

Ticket

Cheapest way to go to Kyoto Cheapest airline ticket to Kyoto http://www.travel.com/info/kyoto Reserve / download e-ticket Travel Typical tour course in Kyoto Spot Information of Nijo castle http://en.wikipedia.org/nijo castle I : Initialize intent and start searching P : Proceed intent evolution E : End searching

Figure 3. Conceptual intent evolution (Query : kyoto travel) Figure 2. Overall System Configuration

3.1 Multiple user-intent model Intent is defined linguistically as a ‘purpose’ or ‘aim.’ These meanings in the Web-search environment are restricted by the constraint ‘user,’ such as ‘the perceived need for information that leads to someone using an information retrieval system in the first place’ [3], ‘user goals in Web search’ [13], or ‘the type of resource desired in the user’s expression to the system’ [10]. With these definitions, we can assume that a user can form his/her intent using words on the Web. To show the efficiency of intent features, we need to conceptualize the intent and its expression method. Let Wq = {wq1, wq2,…wqd} denote query words. Our objective is obtaining intent words Wi = {wi1, wi2, …wid’} where d ≤ d’.3 If d = d’, there is a high probability that a user has intent which is the same as query words. For example, we can easily guess that the dominant intent of the query ‘kyoto travel’ is the need of ‘general information about travel to Kyoto city,’ which is almost same with ‘kyoto travel’ or ‘travel kyoto’ – {kyoto, travel}. Suppose that there are different intents in a query, which means there are cases at d < d’. For a query ‘kyoto travel,’ possible intent expressions are actually more complex such as ‘the cheapest way to travel to Kyoto city,’ need for more specific information such as ‘good hotels when travel Kyoto,’ or for directing to specific Web page such as ‘http://www.travel.com/ japan/kyoto/.’ Assumption 1. User has intent behind the query. With this assumption, we can define Wi = {wi1, wi2, …wid’} = {wq1, wq2,,…wqd, wid+1, wid+2, …wid’} Assumption 2. The Web is sufficient to discover user intent. When the user browses information on the Web, s/he can find intended search results because the Web is the largest corpus of knowledge. 4 With this assumption, user can find almost all possible Wi in the Web by using Wq.

3

This definition is adapted from query refinement tasks, but we exclude the case of d > d’ to simplify the possibilities of finding intent expressions.

4

This assumption has been used implicitly in all intent researches, such as ones based on utilizing user click histories or using words and phrases in language models.

Now we try to find possible intent on the Web, and the subsets of Wi that include Wq can be candidates of intent words, which means there are more than

∑

d' i = d +1 d ' − d

Ci

candidate intent

words. But there can occur semantic duplications in these subsets, such as synonyms, hyponyms / hypernyms and extensions of Wq in wid+1, wid+2, …wid’. In order to eliminate these kinds of overlaps, we could use the concept of intent evolution concerned with user’s knowledge increase over time. This hypothesis is also useful for reducing the number of possible combinations in Wi. Figure 3 shows the example of conceptual intent evolutions. User starts search with a simple query, and finally s/he gets sufficient information on the Web (U1). However, if a user already knew that the cheapest way to go to Kyoto is buying flight ticket from a Web site, s/he issues a query ‘cheapest ticket to Kyoto’ even though the intent of this query is same with the first query ‘kyoto travel’ (U2). Moreover, even though a user has multiple intents with a query, s/he needs overall information for making trip’s plan so the evolution procedures are still useful (U3). We define this conceptual evolution of user intent with the same query as the multiple intent model. Even though Wq is still useful, intent expressions are affected by concerned knowledge, based on which a user decides his/her search actions. In these cases, intent features can be the treated as indicators of these actions. If the user already has a certain amount of knowledge, the query may contain those feature words to reveal his/her actual intent, and the probability of finding meaningful Web information increases. However, when the user is a novice or has no background idea about the query, s/he may want to realize her/his actual information need or its clue(s) by browsing search results concerned with general queries. In all cases, showing concepts of query-intent should be useful and efficient to users.

3.2 Extract Candidate Intent Features The problem is how to efficiently estimate set of possible intent choices for a given query, that is to say, how we can determine wid+1, wid+2, …wid’ using Wq. Commercial search engines deduce a user’s intent by using additional information on the query such as the context of search results or its log data. They then present search results after having conducted prior preprocessing steps such as query expansions or spell corrections. The returned search results are not, however, grouped according to their meaning or potential intent-based categories. They are only arranged by ranking the search results according to pages’ relevance scores.

Pseudo code. Extract head-noun set of question q in question set sj of category cj

Figure 4. Example results of sending a query to YA For extracting features of intent concerned with query, we need to analyze the Web which can be treated as a reflection of Web search intents. Yoon et al. [18] showed that questions in Web Q&A corpus are good expressions to reveal questioners’ intents, so we use these questions coordinated with query to collect candidate intent features. By sending a query to Web Q&A corpus, we can receive useful hints and topics connected to questions that are basis of our intent prediction process. In the example in Figure 4, we show results obtained for the query ‘kyoto travel’ from YA. The question ‘What is the cheapest way to go to Kyoto?’ is included in the category ‘Japan’. It is an example of a particular possible intent that users interested in traveling to Kyoto may have. We can then use not only the category ‘Japan’ but also the expression ‘cheapest way,’ which is the core meaning of the question and is directly connected with category ‘Travel’ for describing this particular user intent. Using a query, questions Q = [q1’, q2’,... qn’] with their matched categories extracted from Web Q&A corpus, C’ = [c1’, c2’,... cn’], are collected. Next, we obtain a set of unique categories appearing in C’ expressed as C = {c1, c2,... cn}, as well as distilled sets of questions by category, S = {s1, s2,... sn} with n ≤ n’. Here si denotes the set of questions included in category ci. From now on we will call categories C = {c1, c2,... cn} as intent categories. But using statistical measurements such as term frequencies is ineffective to find dominant terms in a question in Web Q&A corpus, because only a few terms as well as noisy terms are contained in questions ordinary. Metzler et al. [14] mentioned that the main noun phrase of a sentence often contains the focus of the sentence, and the headword can be thought of as the ‘important’ noun within the phrase. In our study, a question is ordinary a question sentence and the head-noun of a question within a certain intent category is regarded as a key factor of intent feature of that category. We thus decide to use semantic analysis methods to extract intent expressions using questions. Head-noun set extraction method [18] is used in order to detect the core meanings of questions, and regard them as candidate intent features. We assume the first noun (phrase) is the head-noun phrase for each question and extract the adjective of that phrase if there is any. The collected head-noun phrases are assumed to be headnoun sets after stemming. For example, head-noun phrases ‘cheap way’ in the topic ‘travel’ and ‘cheap ways’ or ‘cheapest way’ are treated the same. Finally, the jth intent category has l head-noun

Foreach q in sj POS tagging q to q[] For q[] If (q[i] is Noun) If (q[i+1] is Noun) hnq = q[i] + q[i+1] Else hnq = q[i] End If If (q[i-1] is Adjective) hnq = q[i-1] + hnq End If End If End For Stemming hnq to hnq’ Insert hnq’ into HNcj with count End Foreach sets HNcj = {hncj1, hncj2,... hncjl}. These head noun sets are later used for computing their inclusion within the Web search results. For search results R = [r1, r2,..., rk] of a given query, the feature score of each ith search result is calculated using number of headnoun set inclusion. Here the feature score of a given search result i and intent category j defines the correspondence of that search result to the particular intent category. We use counting the exact matches of head-noun sets in the jth intent category with the ith search result using HNcj and normalizing by the number of headnoun sets in the jth intent category.

Scorefeature(ri , c j ) =

| (termi',ri | termi',ri ∈ HNc j ) |

(1)

| HNc j |

where termi’,ri is the i’ th term in ith search result.

3.3 Distill Real Intent Features We can collect candidate intent features using HNcj from Web Q&A corpus, but we need to determine which candidate intent features are real features. Though we can calculate its possibility by analyzing Web search results e.g. the level of keyword matching of hncij in search results of Wq, there may be matched results which are not the features and, moreover, there may exist meaningful terms even though they have no matching with Wq. To show these semantic concerns, we try to assess feature probability by simulating user’s search behavior. Click Chain Model (CCM) [9] is useful for modeling these patterns. CCM is based on generative process for the user interaction with the search engine results. At each rank, a user starts the examination of the search result from the top ranked document, and chooses whether to click or skip the document according to its perceived relevance. Then the user can choose to continue the examination or abandon the current query session, which depends on her action at current position. CCM makes the following assumptions: 1) Users are homogeneous, which means their information needs are similar for the same query, 2) Decoupled examination and click events, which means user’s snippet examination and clicked events are decoupled, and 3) Cascade examination, which means users

examine search results one by one, starting from higher ranks. We agree with the 2nd and 3rd assumptions, but cannot agree with the 1st. They use this assumption for combining all click data of same query as same relevant results, but our belief is that users are heterogeneous even though their dominant intent can be found with statistical analysis. To make users homogeneous, that is, consider same intent in the same query, we approach from the same search results using different queries. We assume that intents are expressed by terms concerned with the query. If a query clearly represents certain intent, all (or most of) users agree that the intent is well expressed in the query, and its evidence will be directed toward the same search result. In other words, if an intent-clear query is issued, users will be homogeneous. We want to find queries which have intent-clear viewpoint, and assume that semantic supplement of intent features into the original query will guide users to intentclear queries with high probability.

Score feature ( ri , c j ) =

1 ∗ | HN c j |

| HN cj |

∑ I (hn i

i ', cj

)

(5)

i ' =1

4. EXPERIMENTS To the best of our knowledge, there was no specific evaluation method concerned notably with Web search intent. Researchers used relevancy judgment methods such as precision or recall, and binary judged the inclusion of a search result within few numbers of predefined intent topics. However, this method does not fit to our objective because we attempt to find out multiple intents of a query by showing the efficiency of its multiple features. Then we use categorization and re-ranking mechanism to judge the efficiency of intent features by using general, conceptually diverse queries.

4.1 Experimental Setting

Now suppose that users are homogeneous in search results of an intent-clear query. Then the relevance of search results can be treated as decreasing along with their decreased ranks under the given ranking method. The click probability of CCM is useful to assess this model as follows.

We use AOL 500K User Session Collection to choose general queries. Experimental results of our previous work [18] demonstrated the efficiency of intent-based categorization using manual evaluation of search results included. We use this method as the baseline.

Pr(Cli = 1 | Ei = 1, Reli ) = Reli

First, we send 20 queries chosen to YA to collect relevant questions. YA returns questions by keyword matching with a given query, following its relevancy. From the questions for each query returned by YA, we extract head-noun sets of each intent category using a morphological analyzer5 as the candidate intent features. Then the terms within the 50 search results returned by Yahoo!6 for each query are extracted from the returned titles and snippets to matching search results with intent categories extracted. Characteristics of queries are as follows.

(2)

where binary random variables Ei and Cli are used to represent the examination and click event of the document at rank i respectively, and Reli is the relevancy of ith document. The model does not assume any limit in i, but assumes that Ei diminishes exponentially. In CCM, Ri is random. But if we issue intent-clear query, search results’ relevancy is not random, but is decreasing such as Reciprocal Rank (RR) score. We can guess that users give larger intent probability with higher search result ranks, and we should get this probability without user click data. CCM defines the click probability of ith search result when there are no clicks like follows.

Pr(Cl | Reli ) = 1−

2 2

i−1

1+ ( )

Reli , α1 = Pr(Ei+1 = 1| Ei = 1, Cli = 0)

(3)

α1

The click probability is affected by the impact of rank and its relevancy. With this value, we can distill what is the intent feature of a given query. If the click probability of rank i by Wq, PrWq (Cl | Reli ) is smaller than the click probability by Wq and hni’,cj, PrW ∩hn (Cl | Reli ) for the same page, hni’,cj is judged as a q i ',cj feature of given intent category cj. We simply compare ranks of the same URL in search results of Wq and those of Wq∩hni’,cj. Let us define the indicator function I of ith ranked result using hni’,cj.

 1 if PrWq (Cl | Reli ) ≤ PrWq ∩hni ',cj (Cl | Reli ) I i (hni ',cj ) =   0 otherwise

(4)

We aggregate all possible cases within an intent category, and it treats each search result set of a category independently for comparing the click possibility sum of all matched results between Comparing the click possibility sum of all matched results between Wq and Wq∩hni’,cj. Finally, equation (1) is modified like follow.

Table 1. Characteristics of queries (average number) Yahoo! hits

YA hits

65,635,117

175,937

Intent categories in 200 questions 38

Next, we distill real intent features from head-noun sets following our method mentioned in section 3.2 and 3.3, and calculate every item in baseline method again. In this procedure, we compare 30 search results of the query and those of query and candidate intent features. It can be assumed that the more search results collected, the better performance our method has; however, in this case, the noise will also increase. We will try to find out the appropriate number of search results for comparison in further study.

4.2 Evaluation There are 6 evaluation ways in baseline method. Intent scoring by using term frequency (TF), head-noun sets (HN) and Hybrid method are assessed, then scores given are cut by using threshold, 0.25 in our experiment. (TFt , HNt , Hybrid t ) To evaluate our method’s efficiency, we apply our distillation method using CCM (CCM) with every baseline method. Note that CCM model cannot

5

6

An English part-of-speech tagger with bidirectional interface, Tsujii Laboratory, University of Tokyo: http://www-tsujii.s.utokyo.ac.jp http://www.yahoo.com

Table 2. Number of Intent categories

Baseline CCM %

TF

TFt

HN

HNt

36.9 36.9 0.0

13.6 13.6 0.0

20.2 14.9 -26.2

18.5 12.7 -31.4

0.5

Hybrid Hybridt 37.7 14.9 -60.5

17.1 11.8 -31.0

Baseline CCM %

TFt

HN

HNt

0.70 0.70 0.0

0.76 0.76 0.0

0.79 0.77 -2.0

0.76 0.74 -2.0

Hybrid Hybridt 0.68 0.78 +10.0

0.4

0.35

Table 3. Intent category precision TF

0.45

0.76 0.75 -1.0

0.3

0.25 TF

TFt

In Table 2, CCM method decreases the number of intent categories drastically from baseline cases, and as seen in Table 3, CCM method sustains the precision of intent categories (max 0.02), or enhances +0.10 in hybrid setting. These mean that our feature distillation method is generally efficient to find key intent categories. In our observation, CCM methods can efficiently filter out less-useful features within both useful categories and useless ones, sometimes eliminate some categories by excluding its all features, which make number of intent categories decreasing and its precision increasing. Next, we calculate the mean average precision (MAP) to evaluate the intent-based categorization of search results. We employ a binary decision to check whether top 1 to 5 search result(s) are correctly included in given intent categories, and MAP@1 and @5 are calculated to compare intent-based categorization efficiency. As seen in Figure 5 and 6, CCM method ordinary shows better performance than the baseline. There are two cases of decreasing MAP when threshold is applied, which implies that CCM method has different functionality with threshold based method when assess the categorization efficiency.

4.3 Discussion Major efficiency of our proposed method is that it filters out less useful features, then increases the efficiencies when finding intent categories and categorizing search results of the query. In relation to semantic sparseness of the baseline method and too precise intent categories, CCM method can enhance the distilling power using the efficiency of search engines. Based on the experimental results, we can say that Web Q&A corpus is useful to extract intent features of the query. However, actually, the efficiency of our formula depends on the choice of intent features and the ability of semantic analysis. If we were able to find more efficient method for distilling intent features, such as accessing Web indices directly, we could enhance the usefulness

HNt Base

be applied with TF-based evaluation because there are no headnoun sets in TF method. To evaluate the efficiency of intent category extraction, we check the precision of intent categories expressed as the count of correct intent categories within all intent categories collected for each query. Note that the fewer number and higher precision of intent categories, the more efficient user feels. Table 2 and 3 show the analysis results of intent categories.

HN

Hybrid

Hybridt

Hybrid

Hybridt

CCM

Figure 5. MAP@1

0.4

0.35

0.3

0.25

0.2

0.15 TF

TFt

HN

HNt Base

CCM

Figure 6. MAP@5 of our formula. Even though our experimental results depend on YA contents, we do not approach the problem of data span. Regardless of its efficiencies shown, CCM method may show different performance following the choice of materials such as Web Q&A corpus, search engine and machine learning tools, though both distilling categories and search result categorization efficiency can be enhanced due to these choices and easily compared. In our assumptions, query is treated as one of real intent features (when d = d’), but it’s not always true. Sometimes the query is just the broad expression of intent(s). And we use rank comparison between search results of query and those of query and features, but do not concern with query semantics.

5. CONCLUSIONS In this paper, we conceptualize the multiple intent model and suggest novel approach to discover user intent based on finding its features. Initial intent distribution can be calculated by meaning distillation of typed phrases in Web Q&A corpus and assessment method using user behavior in Web searching, called CCM. We extract candidate intent features from a large corpus of online questions in order to find intent categories for a given user query, and distill real intent features using comparison of Web search result sets without human supervision.

Our method’s efficiency is shown in experimental part using feature-based categorizing of search results. The evaluation of our approach indicates enhanced efficiency in distilling key intent categories drastically (max -60.5% filtering and +10.0% precision), and shows better performance for categorizing search results according to possible user intents then baseline methods (MAP@1 = +7% and MAP@5 = +5%). Based on this initial distribution of intent possibilities, we must try to find out practical user intent in query time and accumulate those vectors to supply more confident intent directions to particular user afterwards. In the future, we need to consider precise semantics of queries, and other aspects of data in Web Q&A corpus to extract user intent features, e.g. answers of a question, timestamps of questions and voting result of best answer. We also intend to employ our general intent-based approach for other purposes. For example, one can imagine an intent-based reranking application for Web search results or intent-based browsing and navigation enhancement in the Web. Lastly, we plan to investigate the usefulness of Web Q&A corpus for large scale usage on the Web by analyzing the number of questions and categories as well as their distribution for most popular queries in current query logs.

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