@inproceedings{long-etal-2017-xjnlp,
    title = "{XJNLP} at {S}em{E}val-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model",
    author = "Long, Yu  and
      Li, Zhijing  and
      Wang, Xuan  and
      Li, Chen",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      Cer, Daniel  and
      Jurgens, David",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/S17-2178/",
    doi = "10.18653/v1/S17-2178",
    pages = "1014--1018",
    abstract = "Temporality is crucial in understanding the course of clinical events from a patient{'}s electronic health recordsand temporal processing is becoming more and more important for improving access to content. SemEval 2017 Task 12 (Clinical TempEval) addressed this challenge using the THYME corpus, a corpus of clinical narratives annotated with a schema based on TimeML2 guidelines. We developed and evaluated approaches for: extraction of temporal expressions (TIMEX3) and EVENTs; EVENT attributes; document-time relations. Our approach is a hybrid model which is based on rule based methods, semi-supervised learning, and semantic features with addition of manually crafted rules."
}