SeRT - a tool for knowledge extraction from text
Caroline Barrière School of Information Technology and Engineering University of Ottawa Ottawa, Ontario, Canada
[email protected]
CLiNE - May 24 2002
A few questions... - Why knowledge extraction from text? For building a Knowledge Base... - What’s a Knowledge Base? It depends who defines it.... - From a terminological standpoint: A static repository of domain-specific knowledge, giving the important concepts and their relations. - What kind of relations? Hyperonymy (is-a), meronymy (part-of), synonymy, function, definition, causality - Why start from text? What are the alternatives? CLiNE - May 24 2002
Semantic Relations in Text (SeRT)
- Goal : Starting from a corpus of texts on a specific domain, capture and store the important concepts (terms) of that domain, as well as their relations.
- Hypothesis - definitions can be derived from text analysis - text is used as language and meta-language - paradigmatic relations can be found in texts by pattern search - present knowledge representation formalism allow the representation of this information
CLiNE - May 24 2002
Example of a pattern search for hyperonymy (Corpus on Composting)
In clay soils, organic materials such as compost and pine bark increase drainage and air space. Some yard wastes, such as wood chips, are very difficult to compost fully and are therefore not suitable for incorporation into the soil. Grass clippings and other green vegetation tend to have a higher proportion of nitrogen (and therefore a lower C/N ratio) than brown vegetation such as dried leaves or wood chips. To help meet that requirement, North Carolina passed l law that prohibits depositing organic yard wastes such as leaves, grass clippings, or tree trimmings in the state's landfills. Table 2. Semantic relation hypernym found through the pattern such as and and other
CLiNE - May 24 2002
SeRT - Features - parallel search of terms and relations - term extraction - search for surface patterns leading to semantic relations - focus on user interaction (nothing fully automatic) - term selection and validation - user definition of surface patterns corresponding to semantic relations - user selection of concepts involved (tuple) in the semantic relation - raw text used (no preprocessing necessary) - easy access to KB : save and retrieval - to be used in “bootstrapping” mode
CLiNE - May 24 2002
Term extraction - Usage of a stop list a, able, about, above, according, accordingly, across, actually … - appropriate method for English (but maybe not for French) satellite link - liaison par satellite laser printer - imprimante au laser communication network - réseau de communication - no syntactic analysis - different from: Daille 1994: linguistic patterns (French) Bourigault 1994: morpho-syntactic markers (French) - lemmatization 'moving quickly' ‘mov[ing] quick[ly] 'mov* quick* CLiNE - May 24 2002
Results - Corpus on “composting” - Terms 503 373 258 202 170 155 142 110 103 102 100 92 83
compost pile composting soil materials material nitrogen compost pile water bin time leaves bacteria
402 369 199 187 149 146 133 105 102 96 95 94 85
compost pile soil composting material materials nitrogen compost pile bin time water Compost leaves
CLiNE - May 24 2002
402 369 295 260 199 133 105 105 102 96 95 95 94
compost pile materi* compost* soil nitrogen compost pile temperatur* bin time leav* water Compost
CLiNE - May 24 2002
Search for patterns indicating semantic relations - pre-encoded patterns (earlier work - Barrière 1997) - find list from all other authors - pattern search has multiple possibilities: - string matching - lemmatized token matching - part of speech matching - inclusion of a dictionary look-up (derived from Collins + morphological rules added) - possibility of searching for a pattern around 1 term - usually what Computational Terminologists want to do - display limited or enlarged context
CLiNE - May 24 2002
Example of search patterns Hyperonymy such as (string matching) and other *|n (string + POS) includ* *|n (lemmatized string + POS) *|n is a *|a of [~part] (negative filter) *|y organic materi* [mostly, especially, specifically] (positive filter) + (search with specific term) Synonymy known as also called
(string matching) (string matching)
Meronymy contains *|n is a *|a part of
(string + POS) (string + POS) CLiNE - May 24 2002
CLiNE - May 24 2002
regular dictionary:
77,000
(1046 kb)
26,000
(387 kb)
94,000
(333 kb)
197,000 entries
(1766 kb)
aback,y abactinally,y abashedly,y abdominally,y abed,y abhorrently,y
irregular directory: a',a ablebodied,a ablebodieder,a ablebodiedest,a abranchial,a abranchialer,a abranchialest,a
entries with multiple POS: roughcast,nv huggermugger,anvy broadcast,anvy ground,anv like,acnrvy cut,anv draft,nv
TOTAL:
CLiNE - May 24 2002
public String[][] inflect = // plural nouns { { "", "s" }, { "", "es" }, { "y", "ies" }, { "an", "en" }, { "um", "a" }, { "", "e" }, { "us", "i" }, { ... // comparative adjectives { "", "er" }, { "e", "er" }, { "y", "ier" }, { "c", "caler" }, { "", "der" }, {
CLiNE - May 24 2002
CLiNE - May 24 2002
Information storage in the TKB - transfer of info found at previous step - user selects the terms (concepts) around the pattern - semantic relation / pattern / tuple are stored in the TKB - an uncertainty factor can also be added to the tuple - research on causal relation has lead to realize the necessity of this information - applies to different relations
CLiNE - May 24 2002
Semantic relation extraction
CLiNE - May 24 2002
Results - semantic relations - Exploration of a few patterns - contain? (meronymy) - such as & and other (hypernymy)
Fresh, young weeds from your irrigated garden can contain 60-70% moisture - no need to add water to them. Leaves from eucalyptus, walnuts, and laurel trees contain tannins. Every piece of organic material contains carbon and nitrogen in differing ratios.. Most compost also contains as much as 2 percent calcium. Table 1. Semantic relation meronymfound through the pattern contain
CLiNE - May 24 2002
In clay soils, organic materials such as compost and pine bark increase drainage and air space. Some yard wastes, such as wood chips, are very difficult to compost fully and are therefore not suitable for incorporation into the soil. Grass clippings and other green vegetation tend to have a higher proportion of nitrogen (and therefore a lower C/N ratio) than brown vegetation such as dried leaves or wood chips. To help meet that requirement, North Carolina passed l law that prohibits depositing organic yard wastes such as leaves, grass clippings, or tree trimmings in the state's landfills. Table 2. Semantic relation hypernym found through the pattern such as and and other
CLiNE - May 24 2002
tuple (place 1)
relation < meronym > tuple (place 2)
relation tuple (place 1) tuple (place 2)
60-70% moisture
young weeds
compost
organic material
tannins
leaves from eucalyptus tree
pine bark
organic material
tannins tannins
leaves from walnut tree leaves from laurel tree
wood chips grass clippings
yard wastes green vegetation
carbon
organic material
dried leaves
brown vegetation
nitrogen
organic material
wood chips
brown vegetation
calcium
compost
leaves
organic yard wastes
grass clippings
organic yard wastes
tree trimmings
organic yard wastes
Table 3. Possible relations extracted from a text
CLiNE - May 24 2002
Could we infer is-a relations and extend the type hierarchy?
CLiNE - May 24 2002
SeRT use - Parallel mode - searching on patterns can suggest terms to be explored - search on terms can suggest patterns around them - Bootstrapping mode for relations - start with one pattern: enhance - tuplet compost/soil found used to find other patterns
CLiNE - May 24 2002
As a soil amendment, compost is thought to enhance the physical, chemical, and biological properties of soils. When worm compost is added to soil, it boosts the nutrients available to plants and enhances soil structure and drainage. This discussion is an attempt to enhance your understanding of the conditions which can lead to odor formation, in the hopes that they can be avoided or at least minimized in the future. No matter your soil type, your climatic zone, or your choice of crops, composting will enhance your garden soil, resulting in stronger plants and healthier produce. Table 4. Sentences containing the verbal pattern enhance
CLiNE - May 24 2002
Before using compost, be sure to study a copy of any soil or waste chemical nutrient analyses, pesticide and heavy metal analyses, and stability tests that the producer of the compost performed. When worm compost is added to soil, it boosts the nutrients available to plants and enhances soil structure and drainage. How does compost help soil structure? Some people get around the problem of nitrogen loss by adding bloodmeal to the soil before they bury the compost materials. Composting is really quite simple, inexpensive, ecologically sound, and utterly failproof - no matter what you do, your pile wile eventually rot into soil-enriching compost! While compost is a panacea for all garden soils, poor soils especially will benefit from consistent applications. Table 5. Some examples of the tuple "compost/soil" in the corpus
CLiNE - May 24 2002
Future work
Short term (tool itself)
- Add list of predefined relations & patterns - Add flexibility in pattern search - toward a mix of semantic and syntactic search - Construction of a graphical representation of the semantic network built
CLiNE - May 24 2002
Future work Long term (tool + theoretical background) - Work on compound nouns - much implicit information that could be put explicitly in the KB - Work on representational scheme - the relational database is too limiting - causal relation requires a different type of representation - contexts for expressing the relation (possibly nested) - uncertainty factors - inferencing - Explore pattern search in French - Batch mode extraction (no user) - automatic selection of terms around patterns - after certain terms and patterns have been identified - need an integration of confidence levels on patterns CLiNE - May 24 2002
