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Word sense disambiguation of WordNet glosses

Word sense disambiguation of WordNet glosses. Presenter: Chun-Ping Wu Author: Dan Moldovan, Adrian Novischi. 國立雲林科技大學 National Yunlin University of Science and Technology. 2011/02/10. Computer Speech and Language, 2004. Outline. Motivation Objective WordNet Methodology Experiments

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Word sense disambiguation of WordNet glosses

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  1. Word sense disambiguation of WordNet glosses Presenter: Chun-Ping Wu Author: Dan Moldovan, Adrian Novischi 國立雲林科技大學 National Yunlin University of Science and Technology 2011/02/10 Computer Speech and Language, 2004

  2. Outline • Motivation • Objective • WordNet • Methodology • Experiments • Conclusion • Comments

  3. Motivation • Manual disambiguation is known to be very laborious and time intensive. • It’s difficult to obtain a semantically tagged corpus and the features appearing in such corpus are very sparse, machine learning techniques were not found to be very successful. This is my watch.(手錶?注視?)

  4. Objective • To present a suite of methods and results for the semantic disambiguation of WordNet glosses. This is my watch.(手錶)

  5. WordNet • WordNet • Noun • ISA relation • Verb • Change, communication, cognition, creation, emotion, etc. • Adjective • Synonym/Antonym • Adverb • Synonym/Antonym gloss

  6. Methodology • Semantic disambiguation methods • Monosemous words • Same hierarchy relation • Lexical parallelism • SemCor bigrams • Cross-reference • Reversed cross-reference • Distance among glosses • Common domain • Patterns • First sense restricted • Building the WSD system using the methods

  7. Methodology • Monosemous words • Same hierarchy relation

  8. Methodology • Lexical parallelism • SemCor bigrams

  9. Methodology • Cross-reference • Reversed cross-reference

  10. Methodology • Distance among glosses • Common domain

  11. Methodology • Patterns

  12. Methodology • First sense restricted • A sense of noun or verb is more general if it has the smallest number of ancestors from all senses in the ISA hierarchy. • A sense of an adjective is more general if it has the largest number of similarity pointers from all senses. • Building the WSD system using the methods • XWN_WSD

  13. Experiments • Contribution of each method

  14. Experiments • Voting

  15. Conclusion • Asuite of heuristicalmethods are presented for the disambiguation of WordNet glosses. • Once the WordNet glosses are disambiguated, several applications become possible. • QA System 15

  16. Comments • Advantage • Many samples • Drawback • Some mistakes • Without theoretical basis • Application • WSD, QA System 16

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