Deep Text Matching in Medical Question Answering System.

The retrieval question-answering(Q&A) system based on Q&A library is a system that can retrieve the most similar question from Q&A library to get the correct answer. Classic approaches only use TF-IDF, BM25 and other algorithms to calculate the shallow correlation between the sentences in the input question and the sentences in the Q&A library, without fully considering the semantic information of the sentences. Recently, pre-trained language models have made remarkable achievements in many fields of natural language processing(NLP). In this work we apply the pre-trained language model in the medicine field to the text matching stage of medical question answering system. We also improve and propose a deep text matching model based on BERT, the Potential Topic extraction Medical Bert model(PT-McBERT). We conduct several experiments on the medical text matching dataset CHIP-STS, the results show that our model achieves improvements over the classic methods. Finally, we design a real-world Chinese medical question answering system and apply the optimal model to the question matching stage, which can greatly improve the matching effect of the system.