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.
| Original language | English |
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| Title of host publication | EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 3521-3527 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781952148606 |
| State | Published - 2020 |
| Externally published | Yes |
| Event | 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 - Virtual, Online Duration: 16 Nov 2020 → 20 Nov 2020 |
Publication series
| Name | EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference |
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Conference
| Conference | 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 |
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| City | Virtual, Online |
| Period | 16/11/20 → 20/11/20 |
Bibliographical note
Funding Information:The authors would like to thank the anonymous reviewers for their comments and suggestions. This work was supported in part by grants from Intel Labs, the Israel Science Foundation grant 1951/17 and the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1).
Publisher Copyright:
© 2020 Association for Computational Linguistics