Hypernyms under siege: Linguistically-motivated artillery for hypernymy detection

Vered Shwartz, Enrico Santus, Dominik Schlechtweg

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

75 Scopus citations

Abstract

The fundamental role of hypernymy in NLP has motivated the development of many methods for the automatic identification of this relation, most of which rely on word distribution. We investigate an extensive number of such unsupervised measures, using several distributional semantic models that differ by context type and feature weighting. We analyze the performance of the different methods based on their linguistic motivation. Comparison to the state-of-the-art supervised methods shows that while supervised methods generally outperform the unsupervised ones, the former are sensitive to the distribution of training instances, hurting their reliability. Being based on general linguistic hypotheses and independent from training data, unsupervised measures are more robust, and therefore are still useful artillery for hypernymy detection.

Original languageEnglish
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages65-75
Number of pages11
ISBN (Electronic)9781510838604
DOIs
StatePublished - 2017
Event15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Valencia, Spain
Duration: 3 Apr 20177 Apr 2017

Publication series

Name15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference
Volume1

Conference

Conference15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017
Country/TerritorySpain
CityValencia
Period3/04/177/04/17

Bibliographical note

Publisher Copyright:
© 2017 Association for Computational Linguistics.

Funding

The authors would like to thank Ido Dagan, Alessandro Lenci, and Yuji Matsumoto for their help and advice. Vered Shwartz is partially supported by an Intel ICRI-CI grant, the Israel Science Foundation grant 880/12, and the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1). Enrico Santus is partially supported by HK PhD Fellowship Scheme under PF12-13656.

FundersFunder number
DIPPF12-13656, DA 1600/1-1
German-Israeli Project Cooperation
Intel ICRI-CI
Deutsche Forschungsgemeinschaft
Israel Science Foundation880/12

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