TY - GEN
T1 - Improving temporal relation extraction with training instance augmentation
AU - Lin, Chen
AU - Miller, Timothy
AU - Dligach, Dmitriy
AU - Bethard, Steven
AU - Savova, Guergana
N1 - Funding Information:
Thanks to Sean Finan for technically supporting the experiments. The study was funded by R01 LM 10090 (THYME), R01GM103859 (PGx), and U24CA184407 (DeepPhe). 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:
© BioNLP 2016. All rights reserved.
PY - 2016
Y1 - 2016
N2 - Temporal relation extraction is important for understanding the ordering of events in narrative text. We describe a method for increasing the number of high-quality training instances available to a temporal relation extraction task, with an adaptation to different annotation styles in the clinical domain by taking advantage of the Unified Medical Language System (UMLS). This method notably improves clinical temporal relation extraction, works beyond featurizing or duplicating the same information, can generalize between-argument signals in a more effective and robust fashion. We also report a new state-of-the-art result, which is a two point improvement over the best Clinical TempEval 2016 system.
AB - Temporal relation extraction is important for understanding the ordering of events in narrative text. We describe a method for increasing the number of high-quality training instances available to a temporal relation extraction task, with an adaptation to different annotation styles in the clinical domain by taking advantage of the Unified Medical Language System (UMLS). This method notably improves clinical temporal relation extraction, works beyond featurizing or duplicating the same information, can generalize between-argument signals in a more effective and robust fashion. We also report a new state-of-the-art result, which is a two point improvement over the best Clinical TempEval 2016 system.
UR - http://www.scopus.com/inward/record.url?scp=85120065740&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85120065740
T3 - BioNLP 2016 - Proceedings of the 15th Workshop on Biomedical Natural Language Processing
SP - 108
EP - 113
BT - BioNLP 2016 - Proceedings of the 15th Workshop on Biomedical Natural Language Processing
A2 - Cohen, Kevin Bretonnel
A2 - Demner-Fushman, Dina
A2 - Ananiadou, Sophia
A2 - Tsujii, Jun-ichi
PB - Association for Computational Linguistics (ACL)
T2 - 15th Workshop on Biomedical Natural Language Processing, BioNLP 2016
Y2 - 12 August 2016
ER -