Within-Between Lexical Relation Classification

Oren Barkan, Avi Caciularu, Ido Dagan


Abstract
We propose the novel Within-Between Relation model for recognizing lexical-semantic relations between words. Our model integrates relational and distributional signals, forming an effective sub-space representation for each relation. We show that the proposed model is competitive and outperforms other baselines, across various benchmarks.
Anthology ID:
2020.emnlp-main.284
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3521–3527
Language:
URL:
https://aclanthology.org/2020.emnlp-main.284
DOI:
10.18653/v1/2020.emnlp-main.284
Bibkey:
Cite (ACL):
Oren Barkan, Avi Caciularu, and Ido Dagan. 2020. Within-Between Lexical Relation Classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3521–3527, Online. Association for Computational Linguistics.
Cite (Informal):
Within-Between Lexical Relation Classification (Barkan et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.284.pdf
Video:
 https://slideslive.com/38939060