SynET: Synonym Expansion using Transitivity

Jiale Yu, Yongliang Shen, Xinyin Ma, Chenghao Jia, Chen Chen, Weiming Lu

Abstract
In this paper, we study a new task of synonym expansion using transitivity, and propose a novel approach named SynET, which considers both the contexts of two given synonym pairs. It introduces an auxiliary task to reduce the impact of noisy sentences, and proposes a Multi-Perspective Entity Matching Network to match entities from multiple perspectives. Extensive experiments on a real-world dataset show the effectiveness of our approach.
Anthology ID:
2020.findings-emnlp.177
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1961–1970
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.177
DOI:
10.18653/v1/2020.findings-emnlp.177
Bibkey:
Cite (ACL):
Jiale Yu, Yongliang Shen, Xinyin Ma, Chenghao Jia, Chen Chen, and Weiming Lu. 2020. SynET: Synonym Expansion using Transitivity. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1961–1970, Online. Association for Computational Linguistics.
Cite (Informal):
SynET: Synonym Expansion using Transitivity (Yu et al., Findings 2020)
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