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
- 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)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2020.emnlp-main.284.pdf