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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Tell me your position: Distantly supervised biomedical entity relation extraction using entity position marker.

Jiran Zhu1, Jikun Dong1, Hongyun Du1

  • 1School of Information Science and Engineering, Shandong Normal University, Jinan, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel four-stage model using BioBERT and Multi-Instance Learning to improve biomedical relation extraction from noisy data. The model effectively utilizes entity position markers, significantly outperforming baseline methods and reducing noise.

Keywords:
Biomedical entity relation extractionDeep neural networkDistant supervisionNatural language processingPosition marker

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Advancements in biomedical technologies generate vast textual data.
  • Distant supervision aids in automatic data acquisition but introduces noise, complicating relation extraction.
  • Existing methods often overlook the impact of labeled entity locations.

Purpose of the Study:

  • To develop a robust model for biomedical relation extraction that addresses noise from distant supervision.
  • To investigate the utility of entity position markers in enhancing relation extraction performance.
  • To evaluate the effectiveness of a novel four-stage model integrating BioBERT and Multi-Instance Learning.

Main Methods:

  • A four-stage model incorporating BioBERT and Multi-Instance Learning was developed.
  • Sentences were marked with position information, including global, start, and end markers for entities.
  • Sentence vectors were aggregated using three methods for bag-level feature representation and classification.

Main Results:

  • The proposed model significantly outperformed all baseline models in relation extraction tasks.
  • The model demonstrated effectiveness in noise reduction within biomedical text data.
  • Optimal performance was achieved when bag encoding methods were matched with corresponding sentence encoding representations.

Conclusions:

  • Entity position markers are crucial for improving biomedical relation extraction accuracy.
  • The developed model offers a significant advancement in handling noisy data from distant supervision.
  • Careful selection of sentence and bag encoding strategies is essential for maximizing model performance.