@inproceedings{noriega-atala-etal-2019-understanding,
title = "Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods",
author = "Noriega-Atala, Enrique and
Liang, Zhengzhong and
Bachman, John and
Morrison, Clayton and
Surdeanu, Mihai",
editor = "Nastase, Vivi and
Roth, Benjamin and
Dietz, Laura and
McCallum, Andrew",
booktitle = "Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2603/",
doi = "10.18653/v1/W19-2603",
pages = "21--30",
abstract = "An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i.e., attributing whether the biochemical event is a promotion or an inhibition. Here we present a novel dataset for studying polarity attribution accuracy. We use this dataset to train and evaluate several deep learning models for polarity identification, and compare these to a linguistically-informed model. The best performing deep learning architecture achieves 0.968 average F1 performance in a five-fold cross-validation study, a considerable improvement over the linguistically informed model average F1 of 0.862."
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<abstract>An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i.e., attributing whether the biochemical event is a promotion or an inhibition. Here we present a novel dataset for studying polarity attribution accuracy. We use this dataset to train and evaluate several deep learning models for polarity identification, and compare these to a linguistically-informed model. The best performing deep learning architecture achieves 0.968 average F1 performance in a five-fold cross-validation study, a considerable improvement over the linguistically informed model average F1 of 0.862.</abstract>
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%0 Conference Proceedings
%T Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods
%A Noriega-Atala, Enrique
%A Liang, Zhengzhong
%A Bachman, John
%A Morrison, Clayton
%A Surdeanu, Mihai
%Y Nastase, Vivi
%Y Roth, Benjamin
%Y Dietz, Laura
%Y McCallum, Andrew
%S Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F noriega-atala-etal-2019-understanding
%X An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i.e., attributing whether the biochemical event is a promotion or an inhibition. Here we present a novel dataset for studying polarity attribution accuracy. We use this dataset to train and evaluate several deep learning models for polarity identification, and compare these to a linguistically-informed model. The best performing deep learning architecture achieves 0.968 average F1 performance in a five-fold cross-validation study, a considerable improvement over the linguistically informed model average F1 of 0.862.
%R 10.18653/v1/W19-2603
%U https://aclanthology.org/W19-2603/
%U https://doi.org/10.18653/v1/W19-2603
%P 21-30
Markdown (Informal)
[Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods](https://aclanthology.org/W19-2603/) (Noriega-Atala et al., NAACL 2019)
ACL