Intelligent Database Systems Lab N.Y.U.S.T. I. M. Word sense disambiguation of WordNet glosses Presenter: Chun-Ping Wu Author: Dan Moldovan, Adrian Novischi.

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Presentation transcript:

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Word sense disambiguation of WordNet glosses Presenter: Chun-Ping Wu Author: Dan Moldovan, Adrian Novischi Computer Speech and Language, 2004 國立雲林科技大學 National Yunlin University of Science and Technology 2011/02/10

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Outline Motivation Objective WordNet Methodology Experiments Conclusion Comments 2

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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. 3 This is my watch.( 手錶 ? 注視 ?)

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective To present a suite of methods and results for the semantic disambiguation of WordNet glosses. 4 This is my watch.( 手錶 )

Intelligent Database Systems Lab N.Y.U.S.T. I. M. WordNet  Noun  ISA relation  Verb  Change, communication, cognition, creation, emotion, etc.  Adjective  Synonym/Antonym  Adverb  Synonym/Antonym 5 gloss

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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 6

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Monosemous words Same hierarchy relation 7

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Lexical parallelism SemCor bigrams 8

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Cross-reference Reversed cross-reference 9

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Distance among glosses Common domain 10

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Patterns 11

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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 12

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Contribution of each method 13

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 14 Voting

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion 15 A suite of heuristical methods are presented for the disambiguation of WordNet glosses. Once the WordNet glosses are disambiguated, several applications become possible. QA System

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments 16 Advantage  Many samples Drawback  Some mistakes  Without theoretical basis Application  WSD, QA System