@inproceedings{olex-etal-2019-nlp,
    title = "{NLP} Whack-A-Mole: {C}hallenges in Cross-Domain Temporal Expression Extraction",
    author = "Olex, Amy  and
      Maffey, Luke  and
      McInnes, Bridget",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/N19-1369/",
    doi = "10.18653/v1/N19-1369",
    pages = "3682--3692",
    abstract = "Incorporating domain knowledge is vital in building successful natural language processing (NLP) applications. Many times, cross-domain application of a tool results in poor performance as the tool does not account for domain-specific attributes. The clinical domain is challenging in this aspect due to specialized medical terms and nomenclature, shorthand notation, fragmented text, and a variety of writing styles used by different medical units. Temporal resolution is an NLP task that, in general, is domain-agnostic because temporal information is represented using a limited lexicon. However, domain-specific aspects of temporal resolution are present in clinical texts. Here we explore parsing issues that arose when running our system, a tool built on Newswire text, on clinical notes in the THYME corpus. Many parsing issues were straightforward to correct; however, a few code changes resulted in a cascading series of parsing errors that had to be resolved before an improvement in performance was observed, revealing the complexity temporal resolution and rule-based parsing. Our system now outperforms current state-of-the-art systems on the THYME corpus with little change in its performance on Newswire texts."
}