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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Effective type label-based synergistic representation learning for biomedical event trigger detection.

Anran Hao^1,2, Haohan Yuan¹, Siu Cheung Hui³

¹School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, Singapore.

BMC Bioinformatics

|July 31, 2024

Summary

This summary is machine-generated.

This study introduces the Biomedical Label-based Synergistic representation Learning (BioLSL) model for improved biomedical event trigger detection. BioLSL enhances performance by learning semantic relationships between labels and text, without external resources.

Keywords:

Biomedical event trigger detection Representation learning Semantic relationships Type label semantics

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Area of Science:

Biomedical Natural Language Processing
Computational Biology
Bioinformatics

Background:

Biomedical event trigger detection is challenging due to domain-specific language.
Current models often rely on external resources, limiting their ability to use label semantics.
Label representation learning offers a promising avenue for enhancing trigger detection by leveraging label semantics.

Purpose of the Study:

To propose a novel model, BioLSL, for biomedical event trigger detection.
To effectively utilize event type labels and sentence context for improved detection.
To develop a model that does not rely on external resources.

Main Methods:

Developed the Biomedical Label-based Synergistic representation Learning (BioLSL) model.
Employed a domain-specific transformer for joint encoding of sentences and labels.
Implemented label-based synergistic representation learning to create Label-Trigger Aware Representation (LTAR) and Label-Context Aware Representation (LCAR).

Main Results:

The BioLSL model achieved state-of-the-art performance on three benchmark BioNLP datasets (MLEE, GE09, GE11).
BioLSL outperformed existing baseline models in biomedical event trigger detection.
The model demonstrated strong performance even with limited training data.

Conclusions:

The BioLSL model effectively detects biomedical event triggers without external resources.
Label representation learning and context-aware enhancement are effective strategies for this task.
BioLSL successfully captures semantic linkages between event mentions and type labels, crucial for biomedical texts.