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GUIR at SemEval-2017 Task 12: A Framework for Clinical Temporal Information Extraction Domain Adaptation

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Authors: Sean MacAvaney, Arman Cohan, Nazli Goharian

Appeared in: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval @ ACL 2017)

Links/IDs:
DOI 10.18653/v1/S17-2180 DBLP conf/semeval/MacAvaneyCG17 ACL S17-2180 Google Scholar 7wWfoDgAAAAJ:d1gkVwhDpl0C Semantic Scholar 18aa76296e00294fcd0213996c536f0b424415b5 smac.pub semeval2017-clinicaltempeval

Abstract:

Clinical TempEval 2017 (SemEval 2017 Task 12) addresses the task of cross-domain temporal extraction from clinical text. We present a system for this task that uses supervised learning for the extraction of temporal expression and event spans with corresponding attributes and narrative container relations. Approaches include conditional random fields and decision tree ensembles, using lexical, syntactic, semantic, distributional, and rule-based features. Our system received best or second best scores in TIMEX3 span, EVENT span, and CONTAINS relation extraction.

BibTeX @inproceedings{macavaney:semeval2017-clinicaltempeval, author = {MacAvaney, Sean and Cohan, Arman and Goharian, Nazli}, title = {GUIR at SemEval-2017 Task 12: A Framework for Clinical Temporal Information Extraction Domain Adaptation}, booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation}, year = {2017}, doi = {10.18653/v1/S17-2180}, pages = {1024--1029} }