Abstract: Extracting and modeling temporal information in clinical text is an important element for developing timelines and disease trajectories. Time information in written text varies in preciseness and explicitness, posing challenges for NLP approaches that aim to accurately anchor temporal information on a timeline. Relative and incomplete time expressions (RI-Timexes) are expressions that require additional information for their temporal anchor to be resolved, but few studies have addressed this challenge specifically. In this study, we aimed to reproduce and verify a classification approach for identifying anchor dates and relations in clinical text, and propose a novel relation classification approach for this task.

 

@inproceedings{dupuis-etal-2020-relative,
    title = {Relative and Incomplete Time Expression Anchoring for Clinical Text},
    author = {Dupuis, Louise  and
      Bergou, Nicol  and
      Tissot, Hegler  and
	Velupillai, Sumithra},
    booktitle = {Proceedings of the 3rd Clinical Natural Language Processing Workshop},
    month = nov,
    year = {2020},
    address = {Online},
    publisher = {Association for Computational Linguistics},
    url = {https://www.aclweb.org/anthology/2020.clinicalnlp-1.14},
    pages = {117--129}
}