@inproceedings{bar-haim-etal-2017-improving,
    title = "Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization",
    author = "Bar-Haim, Roy  and
      Edelstein, Lilach  and
      Jochim, Charles  and
      Slonim, Noam",
    editor = "Habernal, Ivan  and
      Gurevych, Iryna  and
      Ashley, Kevin  and
      Cardie, Claire  and
      Green, Nancy  and
      Litman, Diane  and
      Petasis, Georgios  and
      Reed, Chris  and
      Slonim, Noam  and
      Walker, Vern",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W17-5104/",
    doi = "10.18653/v1/W17-5104",
    pages = "32--38",
    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."
}