Syn2Vec: Synset Colexification Graphs for Lexical Semantic Similarity

John Harvill; Roxana Girju; Mark Hasegawa-Johnson

doi:10.18653/v1/2022.naacl-main.386

Syn2Vec: Synset Colexification Graphs for Lexical Semantic Similarity

John Harvill, Roxana Girju, Mark Hasegawa-Johnson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper we focus on patterns of colexification (co-expressions of form-meaning mapping in the lexicon) as an aspect of lexical-semantic organization, and use them to build large scale synset graphs across BabelNet's typologically diverse set of 499 world languages. We introduce and compare several approaches: monolingual and cross-lingual colexification graphs, popular distributional models, and fusion approaches. The models are evaluated against human judgments on a semantic similarity task for nine languages. Our strong empirical findings also point to the importance of universality of our graph synset embedding representations with no need for any language-specific adaptation when evaluated on the lexical similarity task. The insights of our exploratory investigation of large-scale colexification graphs could inspire significant advances in NLP across languages, especially for tasks involving languages which lack dedicated lexical resources, and can benefit from language transfer from large shared cross-lingual semantic spaces.

Original language	English (US)
Title of host publication	Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics
Subtitle of host publication	Human Language Technologies
Editors	Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
Publisher	Association for Computational Linguistics
Pages	5259-5270
Number of pages	11
ISBN (Electronic)	9781955917711
DOIs	https://doi.org/10.18653/v1/2022.naacl-main.386
State	Published - Jul 2022
Event	2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022 - Seattle, United States Duration: Jul 10 2022 → Jul 15 2022

Conference

Conference	2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022
Country/Territory	United States
City	Seattle
Period	7/10/22 → 7/15/22

Keywords

linguistic typology, senses, synsets, semantics, knowledge transfer, cross-lingual, semantic similarity

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Information Systems
Software

Online availability

10.18653/v1/2022.naacl-main.386License: CC BY

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Harvill, J., Girju, R., & Hasegawa-Johnson, M. (2022). Syn2Vec: Synset Colexification Graphs for Lexical Semantic Similarity. In M. Carpuat, M-C. de Marneffe, & I. V. Meza Ruiz (Eds.), Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 5259-5270). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.naacl-main.386

Syn2Vec: Synset Colexification Graphs for Lexical Semantic Similarity. / Harvill, John; Girju, Roxana ; Hasegawa-Johnson, Mark.
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. ed. / Marine Carpuat; Marie-Catherine de Marneffe; Ivan Vladimir Meza Ruiz. Association for Computational Linguistics, 2022. p. 5259-5270.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Harvill, J, Girju, R & Hasegawa-Johnson, M 2022, Syn2Vec: Synset Colexification Graphs for Lexical Semantic Similarity. in M Carpuat, M-C de Marneffe & IV Meza Ruiz (eds), Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, pp. 5259-5270, 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, United States, 7/10/22. https://doi.org/10.18653/v1/2022.naacl-main.386

Harvill J, Girju R , Hasegawa-Johnson M. Syn2Vec: Synset Colexification Graphs for Lexical Semantic Similarity. In Carpuat M, de Marneffe M-C, Meza Ruiz IV, editors, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics. 2022. p. 5259-5270 doi: 10.18653/v1/2022.naacl-main.386

Harvill, John ; Girju, Roxana ; Hasegawa-Johnson, Mark. / Syn2Vec: Synset Colexification Graphs for Lexical Semantic Similarity. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. editor / Marine Carpuat ; Marie-Catherine de Marneffe ; Ivan Vladimir Meza Ruiz. Association for Computational Linguistics, 2022. pp. 5259-5270

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Syn2Vec: Synset Colexification Graphs for Lexical Semantic Similarity

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