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Exploiting syntactic and semantics information for chemical-disease relation extraction.

Huiwei Zhou1, Huijie Deng2, Long Chen2

  • 1School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, People's Republic of China zhouhuiwei@dlut.edu.cn.

Database : the Journal of Biological Databases and Curation
|April 16, 2016
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This study introduces a new method to identify chemical-disease relations (CDR) in text, improving chemical safety research. Combining lexical, syntactic, and semantic models enhances the accuracy of extracting these crucial relationships.

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Toxicology

Background:

  • Identifying chemical-disease relations (CDR) is crucial for chemical safety and toxicity assessments.
  • Existing methods may not fully leverage diverse information sources within biomedical literature.

Purpose of the Study:

  • To propose and evaluate a novel method for chemical-disease relation extraction.
  • To exploit syntactic and semantic information for improved CDR identification.

Main Methods:

  • A hybrid approach combining feature-based, tree kernel-based, and neural network models.
  • Lexical feature exploitation by the feature-based model.
  • Syntactic structure capture by the tree kernel-based model.
  • Semantic representation generation by the neural network model.

Main Results:

  • All three proposed models demonstrated effectiveness in CDR extraction.
  • The combined model, integrating all three approaches, achieved superior extraction performance.
  • Experiments were conducted on the BioCreative V CDR dataset.

Conclusions:

  • The proposed hybrid method effectively extracts chemical-disease relations from biomedical text.
  • Integrating diverse models (lexical, syntactic, semantic) enhances CDR extraction performance.
  • This approach contributes to advancing chemical safety and toxicity studies through improved literature analysis.