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A generalizable NLP framework for fast development of pattern-based biomedical relation extraction systems.

Yifan Peng1, Manabu Torii, Cathy H Wu

  • 1Department of Computer and Information Sciences, University of Delaware, 18 Amstel Ave, Newark, DE 19716, USA. yfpeng@udel.edu.

BMC Bioinformatics
|August 24, 2014
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Summary
This summary is machine-generated.

This study introduces a novel framework for developing biomedical relation extraction systems efficiently. The framework automates pattern generation and simplifies sentences, achieving state-of-the-art results without manual annotation.

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Computational Biology

Background:

  • Biomedical text mining automates information extraction from scientific literature.
  • Relation extraction is crucial for identifying relationships between biological entities.
  • Developing high-performance relation extraction systems is time-consuming and resource-intensive.

Purpose of the Study:

  • To present a novel framework for the rapid development of pattern-based biomedical relation extraction systems.
  • To reduce the need for manual annotation and expert involvement in system design.
  • To improve the efficiency and coverage of biomedical relation extraction.

Main Methods:

  • Systematic generation of extraction patterns leveraging syntactic variations.
  • Application of sentence simplification to enhance pattern coverage.
  • Identification of referential relations for improved extraction accuracy.

Main Results:

  • The developed system achieved high F-scores (e.g., 72.66% for Simple events, 55.57% for Binding events on BioNLP-ST 2011 GE test set).
  • Performance was comparable to top systems on public benchmark datasets (BioNLP-ST 2011 and 2013 GE).
  • Sentence simplification and referential relation linking were identified as key components for effective relation extraction.

Conclusions:

  • A novel framework enables fast development of relation extraction systems.
  • The framework requires only trigger lists, eliminating the need for annotated corpora.
  • This approach significantly reduces the involvement of domain experts in system development.