@inproceedings{ma-etal-2018-crst,
    title = "{CRST}: a Claim Retrieval System in {T}witter",
    author = "Ma, Wenjia  and
      Chao, WenHan  and
      Luo, Zhunchen  and
      Jiang, Xin",
    editor = "Zhao, Dongyan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/C18-2010/",
    pages = "43--47",
    abstract = "For controversial topics, collecting argumentation-containing tweets which tend to be more convincing will help researchers analyze public opinions. Meanwhile, claim is the heart of argumentation. Hence, we present the first real-time claim retrieval system CRST that retrieves tweets containing claims for a given topic from Twitter. We propose a claim-oriented ranking module which can be divided into the offline topic-independent learning to rank model and the online topic-dependent lexicon model. Our system outperforms previous claim retrieval system and argument mining system. Moreover, the claim-oriented ranking module can be easily adapted to new topics without any manual process or external information, guaranteeing the practicability of our system."
}