XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model

Yu Long, Zhijing Li, Xuan Wang, Chen Li


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.
Anthology ID:
S17-2178
Original:
S17-2178v1
Version 2:
S17-2178v2
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
1014–1018
Language:
URL:
https://www.aclweb.org/anthology/S17-2178
DOI:
10.18653/v1/S17-2178
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2178.pdf