Comparing neural models for nested and overlapping biomedical event detection

Nested 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...

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Bibliographic Details
Published in:BMC bioinformatics 2022-06, Vol.23 (1), p.211-211, Article 211
Main Authors: Espinosa, Kurt, Georgiadis, Panagiotis, Christopoulou, Fenia, Ju, Meizhi, Miwa, Makoto, Ananiadou, Sophia
Format: Article
Language:English
Subjects:
Biomedical text
Comparative analysis
Data mining
Event extraction
Medical research
Medicine, Experimental
Methods
Molecular biology
Nested events
Neural networks
Technology application
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Summary:Nested 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. We 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). The 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.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-022-04746-3