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


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
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://aclanthology.org/W19-2603.pdf