@inproceedings{lin-etal-2017-representations,
title = "Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks",
author = "Lin, Chen and
Miller, Timothy and
Dligach, Dmitriy and
Bethard, Steven and
Savova, Guergana",
editor = "Cohen, Kevin Bretonnel and
Demner-Fushman, Dina and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "{B}io{NLP} 2017",
month = aug,
year = "2017",
address = "Vancouver, Canada,",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2341",
doi = "10.18653/v1/W17-2341",
pages = "322--327",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks
%A Lin, Chen
%A Miller, Timothy
%A Dligach, Dmitriy
%A Bethard, Steven
%A Savova, Guergana
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S BioNLP 2017
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada,
%F lin-etal-2017-representations
%X 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.
%R 10.18653/v1/W17-2341
%U https://aclanthology.org/W17-2341
%U https://doi.org/10.18653/v1/W17-2341
%P 322-327
Markdown (Informal)
[Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks](https://aclanthology.org/W17-2341) (Lin et al., BioNLP 2017)
ACL