Research Of Junior High School Mathematics Question And Answer Based On Convolutional Neural Network

With the continuous development of the Internet,users began to use it to ask questions and get answers.The most common Q & A forms are through search engines and Q & A communities,and yet with the massive growth of Internet data,traditional search engines fail to understand users' intentions.As a result,search results often contain a large amount of redundant information,and thus users still need to spend a lot of time and efforts to retrieve the answers they need from the returned results.As the information technology develops rapidly,it is increasingly difficult to meet the needs of users with this approach.In the Q & A community which is based on user interaction and sharing,although less irrelevant information is returned,and questions and answers are more closely connected,this approach cannot give timely response to users,which is not conducive to providing high-quality and convenient Q & A service.In order to address the above problems,this study builds a Q & A model based on Convolutional Neural Network(CNN)and Attention Mechanism,and provides more intelligent and accurate Q & A service for mathematics in junior middle schools by taking Q & A in this course as the starting point.The main work of this study is as follows:1.For Q & A service for mathematics in junior middle schools,this study builds a Convolutional Neural Network model,to extract the semantic features of common questions,transform the common questions into the logical language which can be used for database query,locate relevant data with the knowledge graph of mathematics in junior middle schools,and then return it to the user as Q & A results.2.For the questions and answers which are difficult to deal with in the knowledge graph,the model cannot respond to them because the quantity and scale of knowledge graph are limited and thus the questions and answers are beyond their coverage.Therefore,this thesis uses reading comprehension technology,builds a reading comprehension model,and combines it with search engines,in order to actualize the kind of retrieval Q & A service,as a supplementary module for the Q &A service based on knowledge graph.3.In order to solve the problem that the reading comprehension model cannot fully extract the in-depth semantic features of question and answer texts,this thesis introduces the Attention Mechanism into the reading comprehension model,which enables the model to catch the semantic relation between questions and answers moreeasily and accurately,find the rules between them,and improve the accuracy of the model.In addition,the Recurrent Neural Network(RNN)structure of the reading comprehension model is replaced by the CNN structure,and the main focus of this model is transformed from locating the answer to judging whether the question and answer match or not,which simplifies the calculation process,and at the same time,it makes the model more suitable for mathematical Q & A.The experimental results show that the model has achieved the expected effect in the Q & A task,and also finds some deficiencies,so an integrated model is considered in the future study to enhance the Q & A effect.