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• W4205631594 abstract "Biomedical Named Entity Recognition (BioNER) is often modeled as a sequence labeling task, which assigns the predefined label to each token in given input sequence. Although these sequential labeling models achieve significant achievements, they often fail to give precise boundaries of the named entity. In addition, a vast amount of work focuses more on textual sequence representation but ignores label information. To tackle these problems, in this paper, we directly model span-level named entity recognition, specifically, we treat the BioNER as a joint task of boundary detection and span classification under a multitask framework. In order to enhance boundary supervision, we introduce an entity type label as an additional guide and propose a co-attentive interactive mechanism to improve the span representation. Extensive experiments <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> on four benchmark datasets demonstrate that our proposed method obtains competitive results, achieving 90.26%, 78.04%, 90.21%, and 86.58% on BC5CDR, JNLPBA, NCBI, and BC2GM datasets, respectively, in terms of F1 score." @default.
• W4205631594 created "2022-01-25" @default.
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• W4205631594 date "2021-12-09" @default.
• W4205631594 modified "2023-09-26" @default.
• W4205631594 title "Co-Attentive Span Network with Multi-task learning for Biomedical Named Entity Recognition" @default.
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• W4205631594 doi "https://doi.org/10.1109/bibm52615.2021.9669749" @default.
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