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Domain transformation on biological event extraction by learning methods.

Wen Juan Hou1, Bamfa Ceesay1

  • 1Department of Computer Science and Information Engineering, National Taiwan Normal University, No. 88, Ting Zhou Road, Section 4, Taipei 116, Taiwan, ROC.

Journal of Biomedical Informatics
|June 22, 2019
PubMed
Summary

This study introduces a novel approach for biological event extraction, creating a common dataset using transfer learning to enable domain-independent event and relation extraction. The developed deep learning system shows competitive performance across different biological domains.

Keywords:
Biological eventEvent extractionGRNGRNAMulti-domainNeural networks

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Event extraction and annotation in biological text mining are typically domain-specific.
  • Existing methods lack generalizability across different biological domains.
  • A need exists for domain-independent event and relation extraction systems.

Purpose of the Study:

  • To develop a domain-independent approach for biological event and relation extraction.
  • To create a common dataset applicable to multiple event extraction tasks.
  • To evaluate a deep learning system for cross-domain event extraction.

Main Methods:

  • Utilized transfer learning and structural correspondence learning (SCL) to generate a common dataset from two independent event extraction tasks.
  • Developed a deep learning event extraction system.
  • Employed a classifier model trained on the common dataset for event and relation extraction.

Main Results:

  • The proposed approach successfully generates a versatile dataset for event extraction.
  • The deep learning classifier demonstrated the ability to extract events and relations irrespective of the test input's domain.
  • The system achieved competitive performance compared to domain-specific methods.

Conclusions:

  • A domain-independent approach for biological event extraction is feasible and effective.
  • Transfer learning and SCL can bridge domain gaps in biological IE.
  • The developed system offers a promising solution for broader applications in biological text mining.