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

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Exploiting sequence labeling framework to extract document-level relations from biomedical texts.

Zhiheng Li¹, Zhihao Yang², Yang Xiang³

¹School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China.

BMC Bioinformatics

|March 29, 2020

Summary

This summary is machine-generated.

A new sequence labeling method, Bio-Seq, effectively extracts biomedical relations from text. It improves inter-sentential relation extraction accuracy by considering interactions between relations.

Keywords:

Document-level relation Relation extraction Sequence labeling

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Area of Science:

Biomedical Natural Language Processing
Bioinformatics
Computational Biology

Background:

Biomedical texts contain valuable intra- and inter-sentential semantic relations crucial for research.
Existing methods often neglect inter-sentential relations or fail to capture interactions between relations.
Accurate extraction of document-level relations is challenging due to the complexity of biomedical texts.

Purpose of the Study:

To propose a novel sequence labeling-based method, Bio-Seq, for biomedical relation extraction.
To enhance feature extraction at different levels, particularly for inter-sentential relations.
To leverage interactions between relations for improved document-level relation extraction precision.

Main Methods:

Developed Bio-Seq, a sequence labeling framework extended with multiple specified feature extractors.
Utilized sequence labeling to capture inter-sentential relation features and interactions.
Applied the method to biomedical corpora for relation extraction tasks.

Main Results:

Achieved an F1-score of 63.5% on the BioCreative V chemical disease relation corpus.
Obtained an F1-score of 54.4% on inter-sentential relations, outperforming a document-level classification baseline by 10.5%.
Reached an F1-score of 85.1% on the n2c2-ADE sub-dataset.

Conclusions:

Sequence labeling is a viable approach for document-level biomedical relation extraction.
The proposed Bio-Seq method significantly improves inter-sentential relation extraction performance.
This work advances document-level biomedical text mining research.