Publication
ArgMining 2017
Conference paper

Improving claim stance classification with lexical knowledge expansion and context utilization

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.

Date

08 Sep 2017

Publication

ArgMining 2017

Authors

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