Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization
Roy Bar-Haim, Lilach Edelstein, Charles Jochim, Noam Slonim · 2017
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.