ArticleBMC bioinformatics2022
Comparing neural models for nested and overlapping biomedical event detection.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed, 8 citations in OpenAlex.
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
Abstract
backgroundNested and overlapping events are particularly frequent and informative structures in biomedical event extraction. However, state-of-the-art neural models either neglect those structures during learning or use syntactic features and external tools to detect them. To overcome these limitations, this paper presents and compares two neural models: a novel EXhaustive Neural Network (EXNN) and a Search-Based Neural Network (SBNN) for detection of nested and overlapping events.
resultsWe evaluate the proposed models as an event detection component in isolation and within a pipeline setting. Evaluation in several annotated biomedical event extraction datasets shows that both EXNN and SBNN achieve higher performance in detecting nested and overlapping events, compared to the state-of-the-art model Turku Event Extraction System (TEES).
conclusionsThe experimental results reveal that both EXNN and SBNN are effective for biomedical event extraction. Furthermore, results on a pipeline setting indicate that our models improve detection of events compared to models that use either gold or predicted named entities.
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