TY - GEN
T1 - Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks
AU - Lin, Chen
AU - Miller, Timothy
AU - Dligach, Dmitriy
AU - Bethard, Steven
AU - Savova, Guergana
N1 - Funding Information:
The study was funded by R01LM10090 (THYME), R01GM103859 (iPGx), and U24CA184407 (Deep-Phe). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The Titan X GPU used for this research was donated by the NVIDIA Corporation.
Publisher Copyright:
© 2017 Association for Computational Linguistics
PY - 2017
Y1 - 2017
N2 - Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing. However, they might not be an ideal representation for time expressions, which are long, highly varied, and semantically complex. We describe a method for representing time expressions with single pseudo-tokens for CNNs. With this method, we establish a new state-of-the-art result for a clinical temporal relation extraction task.
AB - Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing. However, they might not be an ideal representation for time expressions, which are long, highly varied, and semantically complex. We describe a method for representing time expressions with single pseudo-tokens for CNNs. With this method, we establish a new state-of-the-art result for a clinical temporal relation extraction task.
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M3 - Conference contribution
AN - SCOPUS:85057221475
T3 - BioNLP 2017 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 16th BioNLP Workshop
SP - 322
EP - 327
BT - BioNLP 2017 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 16th BioNLP Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 16th SIGBioMed Workshop on Biomedical Natural Language Processing, BioNLP 2017
Y2 - 4 August 2017
ER -