Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization

Roy Bar-Haim, Lilach Edelstein, Charles Jochim, Noam Slonim


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
Venues:
ArgMining | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
32–38
Language:
URL:
https://aclanthology.org/W17-5104
DOI:
10.18653/v1/W17-5104
Bibkey:
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., 2017)
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PDF:
https://aclanthology.org/W17-5104.pdf