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Biomedical relation extraction via knowledge-enhanced reading comprehension.

Jing Chen1, Baotian Hu2, Weihua Peng3

  • 1Intelligent Computing Research Center, Harbin Institute of Technology (Shenzhen), Shenzhen, China.

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|January 7, 2022
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Summary
This summary is machine-generated.

This study introduces a knowledge-enhanced reading comprehension (KRC) framework to improve biomedical relation extraction by integrating structured knowledge bases. The KRC framework achieved competitive results on benchmark datasets, enhancing context understanding and knowledge integration.

Keywords:
Biomedical relation extractionKnowledge attention mechanismReading comprehension

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Knowledge Representation

Background:

  • Biomedical relation extraction from literature is crucial but faces challenges in context understanding and knowledge integration.
  • Existing methods often focus on entity pair classification, limiting comprehensive analysis.
  • Machine reading comprehension (RC) offers potential for improved context understanding in this domain.

Purpose of the Study:

  • To develop a novel framework for biomedical relation extraction that integrates reading comprehension and prior knowledge.
  • To reformulate relation extraction as a question-answering task using RC.
  • To enhance the RC framework with knowledge representation for improved accuracy.

Main Methods:

  • Proposed a knowledge-enhanced reading comprehension (KRC) framework.
  • Generated questions for each relation to convert extraction into a question-answering problem.
  • Integrated knowledge representation via an efficient knowledge-enhanced attention mechanism within the RC framework.

Main Results:

  • The KRC framework was evaluated on the BioCreative V CDR and CHR datasets.
  • Achieved competitive document-level F1 scores of 71.18% and 93.3%, respectively.
  • Demonstrated the effectiveness of the proposed approach compared to existing methods.

Conclusions:

  • Open-domain reading comprehension data and knowledge representation significantly improve biomedical relation extraction within the KRC framework.
  • The study encourages further research into combining reading comprehension and biomedical relation extraction.
  • The KRC framework shows promise for advancing the field of biomedical relation extraction.