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COOC

COOC. Practicum / Software Project, SS 2000 Final Report Tanja von den Berg, Tilman Jäger, Kerstin Klöckner, Stephan Lesch, Holger Neis, Norbert Pfleger, Diana Raileanu, Hubert Schlarb Supervisors: Jan Alexandersson, Paul Buitelaar. Contents. Intro Theoretical foundations At the outset

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COOC

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  1. COOC Practicum / Software Project, SS 2000 Final Report Tanja von den Berg, Tilman Jäger, Kerstin Klöckner, Stephan Lesch, Holger Neis, Norbert Pfleger, Diana Raileanu, Hubert Schlarb Supervisors: Jan Alexandersson, Paul Buitelaar

  2. Contents • Intro • Theoretical foundations • At the outset • Project aspects • Preprocessing • Training • Application • Evaluation • Outlook COOC

  3. Intro • Word Sense Disambiguation (WSD) as preparation for semantic analysis of text documents • Application areas: translation systems, info retrieval systems, document classification, etc. • Machine learning approaches: - supervised (semantically tagged corpora) - unsupervised (untagged corpora) • COOC: the first unsupervised, corpus-based approach for German COOC: Einleitung

  4. Theoretical Foundations WSD (Word Sense Disambiguation) in context: E.g.: bank - place to sit vs. financial institution I‘m going to the bank to get some money. COOC: cooccurrence of words in a given context GermaNet: (WordNet for German) WordNet: - lexical and semantic data bank - semantic net, ontology - lexical and conceptual relations (antonymy, hyponymy) COOC: Theoretische Grundlagen

  5. Theoretical Foundations (II) Method: - knowledge sources (WordNet, Thesaurus) - the possibility of finding relations between words and meanings supervised: - requires already disambiguated data - requires large amounts of data unsupervised: - requires even more data - data need not be desambiguated COOC: Theoretische Grundlagen

  6. Theoretical Foundations (III) • Examples of unsupervised methods: • Lesk (1986): comparison among dictionary entries • Yarowski (1992): • - Roget‘s Thesaurus, Groliers Encyclopedia • - collections of contexts for a thesaurus category • - identification of characteristic words • Resnik (1997): - Penn Treebank Corpus, pos-tagged, syntactically annotated • - selectional preference (predicate arguments) COOC: Theoretische Grundlagen

  7. At the outset • Approach of Seligman (94): • Japanese dialogues (direction finding, hotel reservations in spontaneous speech) • thesaurus with 4 fixed abstraction levels • explicit semantic smoothing • COOC project: • Tiger corpus (Frankfurter Rundschau) • GermaNet with varying number of abstraction levels (up to 26) • implicit semantic smoothing COOC: Ausgangssituation

  8. Flow diagram COOC: Training

  9. Preprocessing • Conversion of the training corpus (plain text) into the COOC format • Statistics on GermaNet categories COOC: Vorbehandlung

  10. Resources • Tiger corpus (1.051.446 tokens) - German newspaper text from the Frankfurter Rundschau • TnT tagger(Brants 2000) - statistical Part-of-Speech tagger • Mmorph(Petitpierre & Russell, 1995) - morphological analysis tool • GermaNet: - lexico-semantic network for German (about 25000 nouns, 6000 verbs, 3500 Adjectives) COOC: Vorbehandlung

  11. COOC-Format Philip Glass wurde auf seinen weltweiten Tourneen mit Kassetten und Tonbändern überschüttet. (Phillip Glass was showered with audio tape and cassettes during his wordwide tour.) ... 166 seinen NA PPOSAT167 weltweiten weltweit ADJA [ 113815 113669 111763 111559 ] ... 172 Tonbändern Tonband NN [ 75749 ... 1749365 ] ... [ 75749 ... 144863 ]173 überschüttet überschütten VVPP [ 353400 ... 226602 ] [ 353400 ... 2266023 ] ... COOC: Vorbehandlung

  12. GermaNet Hierarchy COOC: Vorbehandlung

  13. Statistics onGermaNet Categories • Omission of higher-frequency categories • Reduction of computational complexity • Format: Frequency ID(Offset) Synset • Example: 70725 1749365 Objekt_0 43450 369009 Situation_0 ........... 2 843903 Kofferraum_0 1 695036 Intellekt_0_Genius_0 COOC: Vorbehandlung

  14. Segmentation... ...at sentence boundaries: Landesbank schlägt Verträge zwischen Stadt und privaten Investoren vor Überall wird gebuddelt und gemauert. Hamburg erlebt den größten Geschäftsbau-Boom. Jährlich hinzukommen rund 300 000 Quadratmeter an Büroräumen. ...or e.g. after every 3 significant words: Landesbank schlägt Verträge zwischen Stadt und privaten Investoren vor Überall wird gebuddelt und gemauert. Hamburg erlebt den größten Geschäftsbau-Boom. Jährlich hinzukommen rund 300 000 Quadratmeter an Büroräumen. COOC: Training

  15. Windows Text window: n segments with current segment in the middle wider scope than n-grams S(i) S(i+1) S(i+2) S(i+3) S(i+4) W(t) W(t+1) W(t+2) n = 3 COOC: Training

  16. Training: unsupervised Compare Peter goes by train with Diana goes by bike: train and bike should both be VEHICLES; but different ambiguities COOC: Training

  17. Statistics • For a pair of categories: • conditional probability • mutual information • Effect: correct category combinations emerge • statistically COOC: Training

  18. Training: Parameters • Segmentation methods • Window width • limiting calculation time and space requirements: • exclusion of certain POS combinations • only categories in certain frequency intervals • only pairs with frequency > minimum COOC: Training

  19. Application • Actual disambiguation process • input: sentences/text in COOC format, containing ambiguous words • output: disambiguated sentences/words • requires training results COOC: Anwendung

  20. To proceed • Connection to the training data bank • selection of parameters (window and segment size) of the training data bank • Text processing • construction of the initial windows • desambiguation of the current segment • results are written to the Ouput Data COOC: Anwendung

  21. S(i) S(i+1) S(i+2) S(i+3) S(i+4) S(i) S(i+1) S(i+2) S(i+3) S(i+4) To proceed (II) • Window handling: • the middle (current) segment is then disambiguated word by word • at the last segment, the window is moved one segment to the right COOC: Anwendung

  22. To proceed (III) • Handling the words in the middle (current) segment • distinguish significant vs. insignificant words (with and without GermaNet categories) • for significant words, the most probable meaning is computed and output • insignificant words are written unchanged into the Output Data COOC: Anwendung

  23. Probability of the Appeareance of a Category in Context • where: • MI: mutual information • PR: conditional probability • c0: current category • ci: context category COOC: Anwendung

  24. Calculation of the most probable meaning • where: • PC: probability of the appearance of a category given a context COOC: Anwendung

  25. Example: Disambiguation Folklore, Rock, Klassik und Jazz zu vermischen reicht ihnen nicht, sie nutzen die Elektronik und sind sogar dazu übergegangen, Instrumente selbst zu bauen. Not satisfied to merely mix up Folk, Rock, Classical, and Jazz, they make use of Electronic Music as well, and go so far as to build their own instruments. 3002 Rock Rock NN 2 Rock_0 3004 Klassik Klassik NN 1 Klassik_0 3008 vermischen vermischen VVINF 1 vermengen_0_vermischen_0 3009 reicht reichen VVFIN 7 reichen_0 3014 nutzen nutzen VVFIN 2 nutzen_2_nützen_2 3016 Elektronik Elektronik NN 1 Elektronik_0 3023 Instrumente Instrument NN 2 Musikinstrument_0_Instrument_2 3026 bauen bauen VVINF 4 bauen_3 3002 Rock Rock NN [ 39981 ... 3228 ] [ 39981 ... 3228 ] 3004 Klassik Klassik NN [ 221503 ... 221266 ] 3008 vermischen vermischen VVINF [ 643704 643048 ] 3009 reicht reichen VVFIN [ 21538 ] [ 339847 307402 ] [ 581324 ... 568361 ] [ 581324 ... 862674] [ 581324 ... 912753 ] [ 586102 585849 ] [ 588150 ... 586261 ] 3016 Elektronik Elektronik NN [ 405356 ... 383322 ] 3023 Instrumente Instrument NN [ 5357 3228 ] [ 142311 ... 3228 ] 3026 bauen bauen VVINF [ 650176 647379 ] [ 742021 ... 734399 ] [ 743571 ... 734399 ] [ 743710 735354 734399 ] COOC: Anwendung

  26. Evaluation:Comparison Test corpus 1017 Komponisten Komponist NN 1 Komponist_0_Komponistin_0 2010 Möglichkeiten Möglichkeit NN 2 Möglichkeit_2_Eventualität_0 14011 verfügbar verfügbar ADJD 0 14014 machen machen VVINF 6 betätigen_0_treiben_0_machen_0 24006 wirkt wirken VVFIN 6 wirken_2 Evaluation corpus (Negra/Lexsem corpus) 1017 Komponisten Komponist NN Komponist_0_Komponistin_0 2010 Möglichkeiten Möglichkeit NN Möglichkeit_2_Chance_0_Gelegenheit_0 14011 verfügbar verfügbar ADJD unknown 14014 machen machen VVINF unspec 24006 wirkt wirken VVFIN wirken_2 COOC: Evaluation

  27. Meanings in the test corpus 2346 words annotated with 3.1 meanings per word, 1366 of these ambiguous, with average of 4.6 meanings COOC: Evaluation

  28. Results (3 Segments/Window) Sentences Segmentgröße 0(Satz) 2 5 7 10 15 count 1882 trivial 773 hitcount 703 586 688 718 724 720 incorrect 347 523 421 349 357 366 nicht desambiguiert 59 210 52 42 28 23 Precision (alle) [32,3%] 80,97 81,28 79,84 81,03 80,74 80,31 Precision (amb.) [21,7%] 66,95 52,84 62,04 67,30 66,98 66,30 Recall 96,51 88,51 96,88 97,41 98,15 98,41 segment size not disambiguated COOC: Evaluation

  29. Summary • COOC: • is the first unsupervised, corpus-based method of disambiguating semantically ambiguous words for German • goes beyond n-gram statistics • uses plain text, GermaNet, MMorph and a POS tagger • is a tool for unsupervised learning, semantic tagging, and evaluation • first evaluation gives 67,3% (81) precision and 97,4% recall COOC: Zusammenfassung

  30. Outlook • Use of GermaNet 2 (but still need a hand-labeled evaluation corpus) • Repeat experiment with WordNet and Penn Treebank Corpus • Several experiments to determine optimal parameters • Two theses: • lexical disambiguation • general predictions COOC: Ausblick

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