@inproceedings{39905af124fa4e83b98661bf3444a66d,
title = "Extracting Time Expressions from Clinical Text",
abstract = "Temporal information extraction is important to understanding text in clinical documents. Temporal expression extraction provides explicit grounding of events in a narrative. In this work we provide a direct comparison of various ways of extracting temporal expressions, using similar features as much as possible to explore the advantages of the methods themselves. We evaluate these systems on both the THYME (Temporal History of Your Medical Events) and i2b2 Challenge corpora. Our main findings are that simple sequence taggers outperform conditional random fields on the new data, and higher-level syntactic features do not seem to improve performance.",
author = "Miller, {Timothy A.} and Steven Bethard and Dmitriy Dligach and Chen Lin and Savova, {Guergana K.}",
note = "Funding Information: The project described was supported by R01LM010090 (THYME) from the National Library Of Medicine. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Publisher Copyright: {\textcopyright} 2015 Association for Computational Linguistics; ACL-IJCNLP 2015 Workshop on Biomedical Natural Language Processing, BioNLP 2015 ; Conference date: 30-07-2015",
year = "2015",
language = "English (US)",
series = "ACL-IJCNLP 2015 - BioNLP 2015: Workshop on Biomedical Natural Language Processing, Proceedings of the Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "81--91",
booktitle = "ACL-IJCNLP 2015 - BioNLP 2015",
}