Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts

Kosuke Yamada, Tsutomu Hirao, Ryohei Sasano, Koichi Takeda, Masaaki Nagata


Abstract
Dividing biomedical abstracts into several segments with rhetorical roles is essential for supporting researchers’ information access in the biomedical domain. Conventional methods have regarded the task as a sequence labeling task based on sequential sentence classification, i.e., they assign a rhetorical label to each sentence by considering the context in the abstract. However, these methods have a critical problem: they are prone to mislabel longer continuous sentences with the same rhetorical label. To tackle the problem, we propose sequential span classification that assigns a rhetorical label, not to a single sentence but to a span that consists of continuous sentences. Accordingly, we introduce Neural Semi-Markov Conditional Random Fields to assign the labels to such spans by considering all possible spans of various lengths. Experimental results obtained from PubMed 20k RCT and NICTA-PIBOSO datasets demonstrate that our proposed method achieved the best micro sentence-F1 score as well as the best micro span-F1 score.
Anthology ID:
2020.findings-emnlp.77
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
871–877
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.77
DOI:
10.18653/v1/2020.findings-emnlp.77
Bibkey:
Cite (ACL):
Kosuke Yamada, Tsutomu Hirao, Ryohei Sasano, Koichi Takeda, and Masaaki Nagata. 2020. Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 871–877, Online. Association for Computational Linguistics.
Cite (Informal):
Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts (Yamada et al., Findings 2020)
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PDF:
https://aclanthology.org/2020.findings-emnlp.77.pdf
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 2020.findings-emnlp.77.OptionalSupplementaryMaterial.zip