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

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Published on: October 13, 2023

Dictionary-based matching graph network for biomedical named entity recognition.

Yinxia Lou¹, Xun Zhu², Kai Tan³

¹School of Artificial Intelligence, Jianghan University, Wuhan, 430056, China.

Scientific Reports

|December 8, 2023

Summary

This summary is machine-generated.

This study introduces a novel dictionary-based matching graph network for biomedical named entity recognition (BioNER). The new model significantly improves BioNER performance by fully leveraging dictionary information, outperforming current state-of-the-art methods.

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Published on: February 23, 2019

Area of Science:

Biomedical informatics
Natural Language Processing
Machine Learning

Background:

Biomedical named entity recognition (BioNER) is crucial for analyzing biomedical text.
Deep neural networks are common for BioNER, often using biomedical dictionaries.
Existing dictionary methods have performance limitations.

Purpose of the Study:

To propose a novel dictionary-based matching graph network for enhanced BioNER.
To improve the utilization of biomedical dictionary information in entity recognition.

Main Methods:

Developed a dictionary-based matching graph network for BioNER.
Projected dictionary combinations onto a directional graph.
Integrated the matching graph with a bi-directional graph convolutional network (BiGCN).

Main Results:

Achieved significant F1 score improvements on five Bio-NER datasets.
Demonstrated superior performance compared to state-of-the-art models.
Specific improvements include 2.8% on BC2GM, 1.3% on BC4CHEMD, 1.1% on BC5CDR, 1.6% on NCBI-disease, and 0.5% on JNLPBA.

Conclusions:

The proposed dictionary-based matching graph network effectively recognizes biomedical named entities.
This approach offers a superior alternative to simple masked dictionary methods.
The model shows strong potential for advancing biomedical information analysis.