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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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CMBEE: A constraint-based multi-task learning framework for biomedical event extraction.

Jingyue Hu¹, Buzhou Tang², Nan Lyu³

¹Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China.

Journal of Biomedical Informatics

|January 25, 2024

Summary

This summary is machine-generated.

This study introduces a novel framework for biomedical event extraction, improving the identification of complex, nested events by utilizing event constraint information. The approach enhances performance on key biomedical corpora.

Keywords:

Biomedical event extraction Event constraint information Multi-task learning

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Area of Science:

Biomedical Natural Language Processing
Computational Biology
Bioinformatics

Background:

Event extraction is vital in NLP, but biomedical nested events pose challenges due to complex semantic relationships.
Existing methods often overlook the crucial binding connections between these nested biomedical events.

Purpose of the Study:

To develop a unified framework for joint biomedical event trigger and argument extraction.
To enhance the performance of nested biomedical event extraction by leveraging event constraint information.

Main Methods:

A multi-task learning framework (CMBEE) integrating event constraint information using N-tuple event patterns.
Utilizing attention and gating mechanisms for fusing multiple tuple information.
Employing local and global constraint information fusion methods to capture inter-event connections.

Main Results:

Achieved the highest F1 score on the Multilevel Event Extraction Biomedical (MLEE) corpus.
Demonstrated favorable performance on the Genia Event (GE 13) corpus.
Validated the effectiveness of modeling event patterns and constraints for complex event extraction.

Conclusions:

Modeling event patterns and constraints is effective for complex biomedical event extraction.
The proposed fusion strategy enhances the expression of semantic information in multi-event extraction tasks.