Learning to identify the best contexts for knowledge-based WSD

Evgenia Wasserman-Pritsker, William W. Cohen, Einat Minkov

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We outline a learning framework that aims at identifying useful contextual cues for knowledge-based word sense disambiguation. The usefulness of individual context words is evaluated based on diverse lexico-statistical and syntactic information, as well as simple word distance. Experiments using two different knowledge-based methods and benchmark datasets show significant improvements due to context modeling, beating the conventional window-based approach.

Original languageEnglish
Title of host publicationConference Proceedings - EMNLP 2015
Subtitle of host publicationConference on Empirical Methods in Natural Language Processing
PublisherAssociation for Computational Linguistics (ACL)
Pages1662-1667
Number of pages6
ISBN (Electronic)9781941643327
DOIs
StatePublished - 2015
EventConference on Empirical Methods in Natural Language Processing, EMNLP 2015 - Lisbon, Portugal
Duration: 17 Sep 201521 Sep 2015

Publication series

NameConference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing

Conference

ConferenceConference on Empirical Methods in Natural Language Processing, EMNLP 2015
Country/TerritoryPortugal
CityLisbon
Period17/09/1521/09/15

Bibliographical note

Publisher Copyright:
© 2015 Association for Computational Linguistics.

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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