@inproceedings{yamada-etal-2020-sequential,
title = "Sequential Span Classification with Neural Semi-{M}arkov {CRF}s for Biomedical Abstracts",
author = "Yamada, Kosuke and
Hirao, Tsutomu and
Sasano, Ryohei and
Takeda, Koichi and
Nagata, Masaaki",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.77",
doi = "10.18653/v1/2020.findings-emnlp.77",
pages = "871--877",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts
%A Yamada, Kosuke
%A Hirao, Tsutomu
%A Sasano, Ryohei
%A Takeda, Koichi
%A Nagata, Masaaki
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F yamada-etal-2020-sequential
%X 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.
%R 10.18653/v1/2020.findings-emnlp.77
%U https://aclanthology.org/2020.findings-emnlp.77
%U https://doi.org/10.18653/v1/2020.findings-emnlp.77
%P 871-877
Markdown (Informal)
[Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts](https://aclanthology.org/2020.findings-emnlp.77) (Yamada et al., Findings 2020)
ACL