Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings
Changxing Wu, Xiaodong Shi, Yidong Chen, Jinsong Su, Boli Wang
Abstract
We introduce a simple and effective method to learn discourse-specific word embeddings (DSWE) for implicit discourse relation recognition. Specifically, DSWE is learned by performing connective classification on massive explicit discourse data, and capable of capturing discourse relationships between words. On the PDTB data set, using DSWE as features achieves significant improvements over baselines.- Anthology ID:
- P17-2042
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 269–274
- Language:
- URL:
- https://aclanthology.org/P17-2042
- DOI:
- 10.18653/v1/P17-2042
- Cite (ACL):
- Changxing Wu, Xiaodong Shi, Yidong Chen, Jinsong Su, and Boli Wang. 2017. Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 269–274, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings (Wu et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/P17-2042.pdf