CLiNG - May Overview of Research - Computational Terminology - Knowledge extraction from Text - Study of causal relation - Corpus building - Uncertainty - Computer Assisted Language Learning (CALL) - Interdisciplinary project on French Second Language - Text understanding - From speech to sentence

CLiNG - May SeRT - a tool for knowledge extraction from text Caroline Barrière School of Information Technology and Engineering University of Ottawa Ottawa, Ontario, Canada

CLiNG - May 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?

CLiNG - May 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

CLiNG - May Example of a pattern search for hyperonymy (Corpus on Composting )

CLiNG - May 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

CLiNG - May 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*

CLiNG - May Results - Corpus on “composting” - Terms 503compost 373pile 258composting 202soil 170materials 155material 142nitrogen 110compost pile 103water 102bin 100time 92leaves 83bacteria 402compost 369pile 199soil 187composting 149material 146materials 133nitrogen 105compost pile 102bin 96time 95water 94Compost 85leaves 402compost 369pile 295materi* 260compost* 199soil 133nitrogen 105compost pile 105temperatur* 102bin 96time 95leav* 95water 94Compost

CLiNG - May

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

CLiNG - May 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 (string matching) also called (string matching) Meronymy contains *|n (string + POS) is a *|a part of (string + POS)

CLiNG - May

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

CLiNG - May Semantic relation extraction

CLiNG - May Results - semantic relations - Exploration of a few patterns - contain? (meronymy) - such as & and other (hypernymy)

CLiNG - May

Could we infer is-a relations and extend the type hierarchy?

CLiNG - May 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

CLiNG - May

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

CLiNG - May 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