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Updated: Jun 6, 2025

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Biomedical document-level relation extraction with thematic capture and localized entity pooling.

Yuqing Li¹, Xinhui Shao¹

¹Department of Mathematics, College of Sciences, Northeastern University, Shenyang, China.

Journal of Biomedical Informatics

|December 2, 2024

Summary

This summary is machine-generated.

This study introduces Thematic Capture and Localized Entity Pooling (TCLEP) for document-level relation extraction. TCLEP improves identifying relationships between multiple entities in biomedical texts.

Keywords:

Document-level relation extraction Local entity pooling Thematic capture

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Area of Science:

Biomedical informatics
Natural Language Processing

Background:

Document-level relation extraction is complex due to multiple entities and relationships.
Existing graph-based methods struggle with multi-entity challenges.

Purpose of the Study:

To develop a novel approach for document-level relation extraction.
To mitigate the multi-entity problem using localized entity pooling.
To enhance extraction by leveraging document thematic representation.

Main Methods:

Introduced localized entity pooling to identify bridge entities.
Utilized pre-training models for reasoning path representation.
Incorporated thematic capture for document-level understanding.

Main Results:

The TCLEP model achieved Macro-F1 scores of 71.7% (CDR) and 85.3% (GDA).
Integrating modules improved state-of-the-art model performance by 1.5% and 0.2%.

Conclusions:

The proposed TCLEP model effectively addresses document-level relation extraction challenges.
Localized entity pooling and thematic capture enhance model performance.