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Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in

Zhiheng Li1, Zhihao Yang1, Chen Shen1

  • 1School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China.

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Summary

This study introduces a novel deep neural network for clinical relation extraction, improving accuracy by incorporating syntactic structures. The new method enhances the performance of natural language processing in analyzing clinical notes.

Keywords:
Relation extraction - deep learningShortest dependency path

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Area of Science:

  • Computational linguistics
  • Medical informatics
  • Artificial intelligence

Background:

  • Clinical relation extraction is challenging for natural language processing (NLP).
  • Existing deep learning methods often overlook syntactic structures.
  • Improving NLP for clinical notes requires capturing deeper linguistic features.

Purpose of the Study:

  • To develop a deep neural network that models syntactic features for clinical relation extraction.
  • To enhance the performance of NLP in analyzing clinical text.
  • To improve the accuracy of extracting relations between clinical entities.

Main Methods:

  • Proposed a novel neural network architecture incorporating shortest dependency path (SDP) and sentence sequence.
  • Utilized bidirectional long short-term memory (Bi-LSTM) for sequence representation.
  • Employed convolutional neural network (CNN) and Bi-LSTM for SDP representation.
  • Used a fully-connected layer with Softmax for relation classification.

Main Results:

  • The proposed approach achieved significant improvements over baseline methods.
  • Demonstrated the effectiveness of incorporating syntactic structures in deep learning models.
  • The F-measure reached 74.34%, a 2.5% improvement over methods without syntactic features.

Conclusions:

  • A new neural network architecture effectively models SDP and sentence sequence for multi-relation extraction.
  • The approach significantly improves performance on clinical notes.
  • Syntactic structures are crucial for effective deep learning-based relation extraction in clinical text.