Title | ||
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Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts. |
Abstract | ||
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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. |
Year | DOI | Venue |
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2020 | 10.18653/V1/2020.FINDINGS-EMNLP.77 | EMNLP |
DocType | Volume | Citations |
Conference | 2020.findings-emnlp | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Kosuke Yamada | 1 | 0 | 1.35 |
Tsutomu Hirao | 2 | 18 | 4.14 |
Ryohei Sasano | 3 | 117 | 15.48 |
Koichi Takeda | 4 | 0 | 3.38 |
Masaaki Nagata | 5 | 573 | 77.86 |