@article{honnibal-johnson-2014-joint,
    title = "Joint Incremental Disfluency Detection and Dependency Parsing",
    author = "Honnibal, Matthew  and
      Johnson, Mark",
    editor = "Lin, Dekang  and
      Collins, Michael  and
      Lee, Lillian",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "2",
    year = "2014",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/ingest-emnlp/Q14-1011/",
    doi = "10.1162/tacl_a_00171",
    pages = "131--142",
    abstract = "We present an incremental dependency parsing model that jointly performs disfluency detection. The model handles speech repairs using a novel non-monotonic transition system, and includes several novel classes of features. For comparison, we evaluated two pipeline systems, using state-of-the-art disfluency detectors. The joint model performed better on both tasks, with a parse accuracy of 90.5{\%} and 84.0{\%} accuracy at disfluency detection. The model runs in expected linear time, and processes over 550 tokens a second."
}