Research On Question And Answer Matching Technology Of Chinese Medical Community Based On Deep Neural Network

With the development of mobile communication technology,more and more people will search related problems on the Internet through mobile phones,computers and other electronic devices when facing health problems.In response to this phenomenon,major medical platforms have developed multiple online medical question and answer platforms,but facing more and more health problems,doctors ’resources are limited,so how to use existing medical health data to quickly analyze answering questions posed by patients automatically is a key question.In response to these questions,how to choose the correct answers to the medical questions is the key content of building an automatic medical question answering system,so we study a series of methods to try to find the accurate answer corresponding to the question from many candidate answers.This article mainly studies the question and answer matching problem in the field of Chinese medical health,mainly including two key questions: 1.How to accurately represent the text information into a vector form that can be processed by the computer.2.How to build a neural network model which can accurately capture the semantic information in the vector representation of text.For the first problem,we use two text representation methods: "word segmentation" and " character segmentation",and find that for the Chinese medical field,due to the inaccuracy of the current word segmentation tool,the direct representation as a character vector can reduce the dimension of the matrix,reduce memory and computing requirements,and can also improve the accuracy of the final model.For the second question,the matching performance of the traditional CNN,RNN and its variants LSTM and BiGRU neural network models on the cMedQA and cMedQA2 datasets is verified.Then combine a variety of neural network models to build a multiscale convolutional neural network model and BiGRU-CNN model,verify the performance of different combination models on multiple datasets,and analyze the characteristics of different combination models.In order to evaluate and improve the accuracy and generalization ability of the question and answer matching model in the field of medical question and answer,we collect the medical question and answer data from several medical and health websites by using crawler technology,construct the largest Chinese medical health question and answer dataset as far as we know,and verifiy the model mentioned above.Finally,we combine attention pooling with traditional neural network model to construct new combination models BiGRU-ATT and BiGRU-CNN-ATT to verify the performance of different attention pooling combination models on multiple data sets,and prove that the accuracy of the model has been improved to a certain extent after the introduction of attention pooling mechanism,which provides theoretical and algorithmic support for the construction of automatic medical question answering system.