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A Span-graph Neural Model for Overlapping Entity Relation Extraction in Biomedical Texts by Fei, Hao; Zhang, Yue; Ren, Yafeng; Ji, Donghong; Wren, Jonathan is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is A Span-graph Neural Model for Overlapping Entity Relation Extraction in Biomedical Texts about?

## Abstract ## Motivation Entity relation extraction is one of the fundamental tasks in biomedical text mining, which is usually solved by the models from natural language processing. Compared with traditional pipeline methods, joint methods can avoid the error propagation from entity to relation, giving better performances. However, the existing joint models are built upon sequential scheme, and fail to detect overlapping entity and relation, which are ubiquitous in biomedical texts. The main reason is that sequential models have relatively weaker power in capturing long-range dependencies, which results in lower performance in encoding longer sentences. In this article, we propose a novel span-graph neural model for jointly extracting overlapping entity relation in biomedical texts. Our model treats the task as relation triplets prediction, and builds the entity-graph by enumerating possible candidate entity spans. The proposed model captures the relationship between the correlated entities via a span scorer and a relation scorer, respectively, and finally outputs all valid relational triplets. ## Results Experimental results on two biomedical entity relation extraction tasks, in

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Author

Fei, Hao; Zhang, Yue; Ren, Yafeng; Ji, Donghong; Wren, Jonathan

Publisher

Oxford University Press; Oxford University Press (OUP) (ISSN 1367-4803)

Published

2020

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)