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Biomedical named entity normalization via interaction-based synonym marginalization
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DOI：10.1016/j.jbi.2022.104238.png)
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摘要
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Objective: Biomedical named entity normalization (BNEN) is a fundamental natural language processing (NLP) task in the biomedical domain. Many representation learning-based methods have been successfully applied to BNEN in recent years. Most of them encode a given biomedical named entity mention (BNEM) and candidates separately, some of them consider relations between the BNEM and its candidates, however, few consider re-lations among the candidates, which may be useful for BNEN.Material and Methods: In this paper, we propose a novel interaction-based synonym marginalization for BNEN, which can capture both the relations between a given mention and the mention's candidates and that among the candidates, called IA-BIOSYN. In IA-BIOSYN, given a BNEM, a candidate selector is used to obtain the candidates of the BNEM dynamically, then an interaction module is used to model BNEM-candidate relations as well as candidate-candidate relations, and finally a synonym marginalization module is used to determine which candidate(s) the BNEM should be mapped to. To validate the effectiveness of our proposed method, we compare it with other state-of-the-art (SOTA) methods on three public BNEN datasets: NCBI-Disease, BC5CDR-Disease and BC5CDR-Chemical. Results: Our proposed method achieves Acc@1 of 0.9333, 0.9379 and 0.9693 on NCBI-Disease, BC5CDR-Disease and BC5CDR-Chemical, respectively, significantly better than other SOTA methods. Conclusions: Both the relations between a given BNEM and its candidates, and the relations among the candidates are useful for BNEN, and the proposed IA-BIOSYN can capture the two types of relations effectively.
Keyword:
Biomedical named entity normalization
Biomedical named entity -candidate interaction
Candidate -candidate interaction
Synonym marginalization
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