Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods

Enrique Noriega-Atala, Zhengzhong Liang, John Bachman, Clayton Morrison, Mihai Surdeanu

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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.
Anthology ID:
W19-2603
Volume:
Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Vivi Nastase, Benjamin Roth, Laura Dietz, Andrew McCallum
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
21–30
Language:
URL:
https://aclanthology.org/W19-2603
DOI:
10.18653/v1/W19-2603
Bibkey:
Cite (ACL):
Enrique Noriega-Atala, Zhengzhong Liang, John Bachman, Clayton Morrison, and Mihai Surdeanu. 2019. Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods. In Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications, pages 21–30, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods (Noriega-Atala et al., NAACL 2019)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W19-2603.pdf