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A rank-based similarity metric for word embeddings

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Word Embeddings (WE) have recently imposed themselves as a standard for representing word meaning in NLP. Semantic similarity between word pairs has become the most common evaluation benchmark for these representations, with vector cosine being typically used as the only similarity metric. In this paper, we report experiments with a rank-based metric for WE, which performs comparably to vector cosine in similarity estimation and outperforms it in the recently-introduced and challenging task of outlier detection, thus suggesting that rank-based measures can improve clustering quality.1

Original languageEnglish
Title of host publication Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
EditorsIryna Gurevych, Yusuke Miyao
PublisherAssociation for Computational Linguistics (ACL)
Pages552-557
Number of pages6
ISBN (Electronic)9781948087346
DOIs
Publication statusPublished - Jul 2018
Externally publishedYes
Event56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, Australia
Duration: 15 Jul 201820 Jul 2018

Publication series

NameACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
Volume2

Conference

Conference56th Annual Meeting of the Association for Computational Linguistics, ACL 2018
Country/TerritoryAustralia
CityMelbourne
Period15/07/1820/07/18

ASJC Scopus subject areas

  • Software
  • Computational Theory and Mathematics

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