Abstract
We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization. Our approach uses a recursive neural network and a newly proposed attention mechanism to compute a representation of the text that focuses on salient content, from the perspective of both RST and the task. Experiments consider variants of the approach and illustrate its strengths and weaknesses.- Anthology ID:
- P17-1092
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 996–1005
- Language:
- URL:
- https://aclanthology.org/P17-1092
- DOI:
- 10.18653/v1/P17-1092
- Cite (ACL):
- Yangfeng Ji and Noah A. Smith. 2017. Neural Discourse Structure for Text Categorization. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 996–1005, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Neural Discourse Structure for Text Categorization (Ji & Smith, ACL 2017)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/P17-1092.pdf
- Code
- jiyfeng/disco4textcat