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IARNN-Based Semantic-Containing Double-Level Embedding Bi-LSTM for Question-and-Answer Matching.

Chang-Zhu Xiong1, Minglian Su1

  • 1College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.

Computational Intelligence and Neuroscience
|April 5, 2019
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A novel semantic-containing double-level embedding Bi-LSTM model (SCDE-Bi-LSTM) improves Chinese medical question-answering (Q&A) matching. This approach enhances accuracy by 14%, achieving 79.15% top-1 accuracy.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Biomedical Informatics

Background:

  • Traditional Question-Answering (Q&A) systems struggle with deep semantic understanding and accurate word segmentation in specialized domains like Chinese medicine.
  • Existing methods often fail to fully leverage sentence semantics or are susceptible to errors introduced by Chinese word segmentation.
  • Attention mechanisms can introduce feature deviation, impacting performance in complex Q&A tasks.

Purpose of the Study:

  • To introduce a novel end-to-end framework, the semantic-containing double-level embedding Bi-LSTM model (SCDE-Bi-LSTM), for improved Chinese medical Q&A matching.
  • To address limitations in current Q&A similarity calculations by incorporating deep semantic information.
  • To mitigate errors from Chinese word segmentation and refine feature extraction in Q&A systems.

Main Methods:

  • Developed a semantic-containing text similarity calculation method for the Q&A core module.
  • Implemented a double-level embedding sentence representation to reduce word segmentation errors.
  • Proposed an improved Bi-LSTM based algorithm for feature extraction to address attention mechanism deviations.
  • Created extensive Chinese medical Q&A corpora for model validation.

Main Results:

  • The SCDE-Bi-LSTM model significantly outperforms strong baselines on Chinese medical Q&A datasets.
  • Achieved a top-1 accuracy improvement of up to 14%, reaching 79.15% on the medical corpora.
  • Demonstrated the framework's applicability across different domains, including the insuranceQA dataset.

Conclusions:

  • The proposed SCDE-Bi-LSTM framework effectively enhances Q&A matching accuracy in the Chinese medical domain by integrating semantic information and improving representation.
  • The novel methods for similarity calculation, sentence embedding, and feature extraction contribute to superior performance over existing state-of-the-art approaches.
  • The framework's robustness and adaptability suggest its potential for broader applications in specialized domain Q&A systems.