@inproceedings{ulinski-etal-2018-using,
    title = "Using Hedge Detection to Improve Committed Belief Tagging",
    author = "Ulinski, Morgan  and
      Benjamin, Seth  and
      Hirschberg, Julia",
    editor = "Blanco, Eduardo  and
      Morante, Roser",
    booktitle = "Proceedings of the Workshop on Computational Semantics beyond Events and Roles",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-1301/",
    doi = "10.18653/v1/W18-1301",
    pages = "1--5",
    abstract = "We describe a novel method for identifying hedge terms using a set of manually constructed rules. We present experiments adding hedge features to a committed belief system to improve classification. We compare performance of this system (a) without hedging features, (b) with dictionary-based features, and (c) with rule-based features. We find that using hedge features improves performance of the committed belief system, particularly in identifying instances of non-committed belief and reported belief."
}