Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts
Kosuke Chris Yamada, Tsutomu Hirao, Ryohei Sasano, Koichi Takeda, Masaaki Nagata · 2020
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-F 1 score as well as the best micro span-F 1 score.