Modeling word meaning in context with substitute vectors

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

39 Scopus citations

Abstract

Context representations are a key element in distributional models of word meaning. In contrast to typical representations based on neighboring words, a recently proposed approach suggests to represent a context of a target word by a substitute vector, comprising the potential fillers for the target word slot in that context. In this work we first propose a variant of substitute vectors, which we find particularly suitable for measuring context similarity. Then, we propose a novel model for representing word meaning in context based on this context representation. Our model outperforms state-of-the-art results on lexical substitution tasks in an unsupervised setting.

Original languageEnglish
Title of host publicationNAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages472-482
Number of pages11
ISBN (Electronic)9781941643495
DOIs
StatePublished - 2015
EventConference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015 - Denver, United States
Duration: 31 May 20155 Jun 2015

Publication series

NameNAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference

Conference

ConferenceConference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015
Country/TerritoryUnited States
CityDenver
Period31/05/155/06/15

Bibliographical note

Publisher Copyright:
© 2015 Association for Computational Linguistics.

Funding

FundersFunder number
Israel Science Foundation880/12

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