Abstract
Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications. It has been addressed in the literature either as a classification task to a set of pre-defined relations or by producing free text paraphrases explicating the relations. Most existing paraphrasing methods lack the ability to generalize, and have a hard time interpreting infrequent or new noun-compounds. We propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrases. Our model helps improving performance on both the noun-compound paraphrasing and classification tasks.- Anthology ID:
- P18-1111
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1200–1211
- Language:
- URL:
- https://aclanthology.org/P18-1111
- DOI:
- 10.18653/v1/P18-1111
- Cite (ACL):
- Vered Shwartz and Ido Dagan. 2018. Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1200–1211, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations (Shwartz & Dagan, ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P18-1111.pdf
- Code
- vered1986/panic