We propose a novel end-to-end approach, namely, the semantic-containing double-level embedding Bi-LSTM model (SCDE-Bi-\nLSTM), to solve the three key problems of Q&A matching in the Chinese medical field. In the similarity calculation of the Q&A core\nmodule, we propose a text similarity calculation method that contains semantic information, to solve the problem that previous Q&A\nmethods do not incorporate the deep information of a sentence into the similarity calculations. For the sentence vector representation\nmodule, we present a double-level embedding sentence representation method to reduce the error caused by Chinese medical word\nsegmentation. In addition, due to the problem of the attention mechanism tending to cause backward deviation of the features, we\npropose an improved algorithm based on Bi-LSTM in the feature extraction stage. The Q&A framework proposed in this paper not\nonly retains important timing features but also loses low-frequency features and noise. Additionally, it is applicable to different\ndomains. To verify the framework, extensive Chinese medical Q&A corpora are created. We run several state-of-the-art Q&A\nmethods as contrastive experiments on the medical corpora and the current popular insuranceQA dataset under different performance\nmeasures. The experimental results on the medical corpora show that our framework significantly outperforms several\nstrong baselines and achieves an improvement of top-1 accuracy of up to 14%, reaching 79.15%.
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