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Extraction of Ontological Information from Lexicon and Corpora

Extraction of Ontological Information from Lexicon and Corpora. Dimitrios Kokkinakis Maria Toporowska Gronostaj. Motto. To process information you need information P. Vossen, 2003. Content. Introduction Background Language resources Methodology

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Extraction of Ontological Information from Lexicon and Corpora

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  1. Extraction of Ontological Information from LexiconandCorpora Dimitrios Kokkinakis Maria Toporowska Gronostaj

  2. Motto To process information you need information P. Vossen, 2003

  3. Content • Introduction • Background • Language resources • Methodology • Lexicon-driven extraction of ontological data • Corpus-driven extraction of ontological data • Conclusions

  4. Background • What is ontological information ? information necessary for making common-sense-like inferences based on our knowledge of the world • How is it represented? in form of structured sets of conceptual types often inclusive semantic relations underlying them • Where? SIMPLE-ontology, EWN, LexiQuest

  5. Background Why is ontological information relevant for NLP? • promotes development of lexicon resources which aim at text-understanding as it offers disambiguation means • provides knowledge needed in • machine translation (MT) • information retrieval (IR) • information extraction (IE) • summarization • computer aided language learning (CALL) • enables communication on the Semantic Web

  6. Background What is meant with a semi-automatic extraction of OI? • some human intervention is involved in information processing to maximize its effects What will we achieve with it? • enhance the content of the Swedish SIMPLE lexicon in a quick and costs-effective way • investigate lexicon-driven and corpus-driven methodologies

  7. Methodology in general (1) Methodological assumptions: • lexical databases, MRD lexica and corpora can be mined for ontological information • relevant factors in information processing : resource size degree of extractability implicitness and explicitness of information bootstrapping

  8. Methodology in general (2) Approach: text data mining (TDM) TDM is a process of exploratory data analysis using text that leads to the discovery of heretofore unknown information, or to answers to questions for which the answer is not currently known (Mitkov 2003, Hearst 2003) Result: evolutionary lexicon model output data are reused to discover new data, which leads to a successive enlargement of lexicon

  9. Language resources SIMPLE-SE (1) Corpora 150 million words i Språkbanken Lexicon resources SIMPLE-SE lexicon GLDB Göteborg lexical database SEMNET

  10. Language resources SIMPLE-SE (2) About SIMPLE-SE • computational lexicon with explicit ontological information (OI) • 10 000 lexicon units • 7 000 nouns, 2 000 verbs, 1 000 adjectives • manually annotated with semantic and OI which is linked to the morphosyntactic information in the PAROLE lexicon • multidimensional

  11. Language resources SIMPLE-SE (3) SIMPLE-SE supports • word sense disambiguation kastanji 1/1/0 FRUIT kastanji 1/1/1 PLANT kastanji 1/1/2sms COLOUR kastanji 1/1/3 FOOD kastanji 1/2/0 ORGANIC OBJECT • finding regular polysemy • creating multilingual links between lexicons

  12. Language resources SIMPLE-SE (4) • SIMPLE-SE supports: • text annotation • text data mining & knowledge based information processing • evaluation • pattern matching based on the ontological information assigned to arguments (selection restrictions/preferences)

  13. Language resources SIMPLE-SE (5) • selection restriction based pattern matching Word/expression Position Ontological term injicera (inject) object Substance bebo (inhabit) object Area griljera (roast) object Food förlova sig (become engaged) subj., prep. obj Human devalvera (devaluate) obj. Money ha ont i (have pain in) prep. obj. Body part

  14. Language resources GLDB Göteborg lexical database, GLDB • 67 000 core senses with stringent definition format • implicit, but extractable genus proximum (genus word) • implicit onto info about arguments in definition extensions • 35 000 explicit semantic references on semantic relations like synonymy, antonymy, hyperonymy, hyponymy and cohyponymy

  15. Language resources SEMNET (1) SEMNET hyperonymic taxonomy • Extraction of hyperonymy relations from GLDBs definitions • (methodology & software Y. Cederholm, 1999) • Recognition of headwords (genus proximum) in definitions

  16. Language resources SEMNET (2) Input data: GLDB definitions 44 915 noun lexeme 10 082 verb lexeme Two analysis methods which complete each other

  17. Language resources SEMNET (3) Method I • distinguishing typical def. patterns for core senses (see overhead/handout from Cederholm Y. 1999, Tabell 1. Definitionsformler)) • pattern matching against non-lemmatized definitions (using regular expressions)

  18. Language resources SEMNET (4) Method II • Input: lemmatized definitions • Assumptions: • genus word is the first word in the definition which matches the part of speech of the headword, the word being defined • method II finds even those genus words which cannot be parsed with the method I

  19. Language resources SEMNET (5) • Analysis results for nouns tot. number of analysis tot. number of correct analysis Method I 8127 (64%) 7141 (56%) Method II 12 194 (95%) 8974 (70%) Method I + II 12 528 (98%) 10536 ( 83%) (evaluation based on 12 786 manually annotated noun genus words) • Approximated result for ca 45 000 nouns i genus position: 36 500 correctly recognised noun genus words

  20. Language resources SEMNET (6) The 33 most frequent noun genus words i SEMNET 2702 person 858 typ 612 del 461 anordning 314 område 261 kvinna 228 tillstånd 219 lära 217 titel 207 grupp 183 föremål 173 sammanfattning 172 mängd 169 sätt 167 plats 166 system 165 växt 162 ämne 153 apparat 145 förmåga 133 medlem 128 språk 122 stycke 122 redskap 122 plats 119 känsla 118 form 116 metod 116 handling 113 enhet 111 ljud 110 instrument 102 verksamhet

  21. Language resources SEMNET (7) Hyperonymy taxonomysjukdom -- [1] akutfall 1/1 -- [2] almsjuka 1/1 -- [3] astma 1/1 -- [4] avitaminos 1/1 -- [5] basedow 1/1 -- [6] bladrullsjuka 1/1 -- [7] blodkräfta 1/1 -- [8] blodsjukdom 1/1 -- [9] blödarsjuka 1/1............................................ (totalt 66 hyponyms)

  22. Definition-driven extraction of ontological information (1) Resources: SIMPLE-SE + SEMNET + GLDB Methodological assumptions • Hyperonymic taxonomy in combination with ontological information in SIMPLE-SE supports semiautomatic extraction of ontological information Procedure: • Preparatory phase relevant for all ontological processing: annotate GLDB data with the ontol. info from the SIMPLE-SE to generate ontologically enriched SEMNET

  23. Definition-driven extraction of ontological information (2) Methodological assumptions (cont.) • The extracted ontological information is an approximation of ontological category until verified with other methods, t.ex. a corpus-driven methodology, semantic/ontological data från GLDB or pattern matching based on selection restrictions • Since annotated words in SIMPLE cover both hyperonyms and hyponyms, two methods are proposed here that put in focus each of these semantic categories

  24. Definition-driven extraction of ontological information (3) Method I: from annotated hyponyms to new annotations of hyperonyms Assumption • One can approximate ontological category of a hyperonym given some information on its hyponyms and using the structural knowledge inherent in ontology • Annotation of a hyperonym can be performed if all of the annotated hyponyms share the same ontological tag or if the tags share a common superordinate tag, except the tag Entity which is ontologically heterogeneous and thus relatively uninformative

  25. Definition-driven extraction of ontological information (4) Method I example Hyponyms known info diabetes [Disease] cat [Air animal], asthma [Disease] dog [Air animal] cholera [Disease] fisk [Water_animal] Hyperonym new info disease =>[Disease] djur => [Animal]

  26. Definition-driven extraction of ontological information (5) Method II from annotated hyperonyms to new annotations of hyponyms Assumption (resulting in approximation) Direct hyponyms (hyponyms which are directly subordinated to the genus word/hyperonym) automatically inherit the ontological category of their hyperonyms och therefore manual annotation of the most frequent genus words/hyperonyms can be recommended and justified. hyperonym known info hyponyms new info myntenhet [Money] => dollar, krona, pund, rubel... [Money]

  27. Definition-driven extraction of ontological information (6) The assumption has far reaching consequences for all those annotated hyponymic words which also occur as genus words, since their subordinates can automatically inherit the ontological class from the hyperonym/genus word. Cascade effect: sjukdom (disease) 66 hyponymes + infektionssjukdom 25 hyponyms + könsjukdom 4 hyponyms

  28. Definition-driven extraction of ontological information (7) Cascade distribution of the ontological type [Animal] Djur 102 hyponyms + hovdjur 10 + ryggradsdjur 8 + fågel 98 + däggdjur 18 Note: 80 most frequent genus words, when ontologically annotated, give rise to 11 000 automatically annotated genus words at the first hyponymy level. This number further increases due to the cascade effect.

  29. Definition-driven extraction of ontological information (8) 2702 person 1/1 person HUMAN 461 anordning 1/*1 device ARTIFACT 314 område 1/1 area AREA (>LOCATION) 261 kvinna 1/1 woman HUMAN 238 tillstånd 1/* state STATE 219 lära 1/*1 doctrine DOMAIN 217 titel 1/*1 titel SOCIAL_STATUS (>HUMAN) 183 föremål 1/* thing CONCRETE_ENTITY 169 sätt 1/*1 manner CONSTITUTIVE 167 plats 1/*1el 4 place LOCATION 166 system 1/*1 system CONSTITUTIVE 165 växt 1/*1 plant PLANT

  30. Conclusion • Ontological annotations are approximations. They need to be verified against manually annotated data and/or by means of corpus-driven methodology for extracting ontological information • The status of ontological annotations need to be explicitly specified in the database • Method I (from hyponyms to hyperonyms) seem to complement the method II (from hyperonyms to hyponyms) since the range of annotated categories increases rapidly • The quality (and quantity) of the used lexical resources determines the precision of the acquired results – ontology

  31. Conclusion cont’d To prevent overgenerating of incorrect ontological annotation special attention needs to be paid to: • disambiguation of polysemous and homographic genus words (hyperonyms) krona [Artifact], [Money], [Part] • analysis of compound nouns gosedjur [Artifact] vs husdjur [Animal]

  32. References Cederholm Y. 1999. Automatisk konstruktion av en hyperonymitaxonomi baserad på definitioner i GLDB. In Från dataskärm och forskarpärm. MISS 25. Göteborgs universitet. Hearst, M. 2003. Text Data Mining. In ed. R. Mitkov The Oxford Handbook of Computational Linguistics Oxford. Mitkov, R. 2003. The Oxford Handbook of Computational Linguistics Oxford. Oxford University Press. Vossen, P. 2003. Ontologies. In ed. R. Mitkov The Oxford Handbook of Computational Linguistics Oxford. about SIMPLE see http://spraakbanken.gu.se

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