Abstract
Stance classification is a core component in on-demand argument construction pipelines. Previous work on claim stance classification relied on background knowledge such as manually-composed sentiment lexicons. We show that both accuracy and coverage can be significantly improved through automatic expansion of the initial lexicon. We also developed a set of contextual features that further improves the state-of-the-art for this task.- Anthology ID:
- W17-5104
- Volume:
- Proceedings of the 4th Workshop on Argument Mining
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Ivan Habernal, Iryna Gurevych, Kevin Ashley, Claire Cardie, Nancy Green, Diane Litman, Georgios Petasis, Chris Reed, Noam Slonim, Vern Walker
- Venue:
- ArgMining
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 32–38
- Language:
- URL:
- https://aclanthology.org/W17-5104
- DOI:
- 10.18653/v1/W17-5104
- Cite (ACL):
- Roy Bar-Haim, Lilach Edelstein, Charles Jochim, and Noam Slonim. 2017. Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization. In Proceedings of the 4th Workshop on Argument Mining, pages 32–38, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization (Bar-Haim et al., ArgMining 2017)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W17-5104.pdf