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CapsTM: capsule network for Chinese medical text matching.

Xiaoming Yu1, Yedan Shen2, Yuan Ni3

  • 1School of Political Science and Public Management, WuHan University, Wuhan, China.

BMC Medical Informatics and Decision Making
|July 31, 2021
PubMed
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This study introduces CapsTM, a novel deep learning model for text matching, outperforming existing methods in Chinese medical question matching. CapsTM utilizes capsule networks to improve feature representation and handle complex relationships in text data.

Area of Science:

  • Natural Language Processing (NLP)
  • Deep Learning
  • Artificial Intelligence

Background:

  • Text Matching (TM) is a core NLP task crucial for information retrieval, question answering, and more.
  • Deep learning models, particularly Convolutional Neural Networks (CNNs), have advanced TM but struggle with small datasets and feature structure preservation.
  • Capsule networks offer a promising alternative to address CNN limitations in TM.

Purpose of the Study:

  • To propose and evaluate CapsTM, a novel deep learning architecture for Text Matching.
  • To leverage capsule networks to overcome the limitations of CNNs in handling small samples and preserving feature structures.
  • To assess the effectiveness of CapsTM on a specialized dataset of Chinese medical questions.

Main Methods:

Keywords:
Capsule networkChinese medical question matchingDeep learningText matching

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  • Developed CapsTM, a five-layer neural network incorporating capsule networks.
  • Employed Bidirectional Long Short-Term Memory (BiLSTM) and attention mechanisms for text representation and interaction.
  • Extended the ESIM model by integrating a capsule layer prior to the prediction layer for enhanced feature extraction.

    • Main Results:

    • A new corpus of 36,360 Chinese medical question pairs was created for evaluation.
    • CapsTM achieved the highest F-score of 0.8666 on the test set, outperforming state-of-the-art methods.
    • Experimental results validate the efficacy of CapsTM on the Chinese medical question matching task.

    Conclusions:

    • CapsTM demonstrates superior performance in Chinese medical question matching compared to existing approaches.
    • The integration of capsule networks enhances the model's ability to capture complex textual relationships.
    • CapsTM represents a significant advancement in applying deep learning to specialized text matching domains.