Research On Semantic Matching Algorithm Of Table Tennis Question Answering System Based On Knowledge Graph

With the development of natural language processing technology,question answering systems in specific fields such as finance,medical treatment,e-commerce,and stylistic entertainment have developed rapidly.Semantic matching is an important research topic in the research of question answering systems.The result of semantic matching directly affects Performance of Question Answering System.This paper takes the semantic matching algorithm of the question and answer system of intelligent table tennis training system as the key research point,and realizes the construction of knowledge map of table tennis question and answer system,and designs the architecture of intelligent table tennis training system based on knowledge map question and answer system.Aiming at the characteristics of the system architecture,a method of using a problem template in the system for problem representation is proposed,and a probability generation model parameter is used to solve the matching algorithm,and the semantic matching process is transformed into a parameter estimation process of the probability generation model and the answer is retrieved.The main research contents of this article include:(1)Aiming at the database construction of the intelligent table tennis question and answer system,this article collects question and answer corpora in the field of table tennis and realizes the construction of the knowledge map of the table tennis question and answer system.(2)In response to the requirements of intelligent question answering in the intelligent table tennis training system,this article designs the architecture of the intelligent table tennis question answering system,analyzes the function implementation of specific modules in the system,proposes the use of question representation methods based on question templates,and is based on the question answering system architecture Using the probability generation model,the semantic matching process is transformed into the parameter estimation process of the probability generation model,and the answer matching function of the question answering system is realized by estimating the parameters of the probability generation model.(3)Aiming at the parameter estimation problem of probabilistic generative model,this paper proposes to use the maximum likelihood estimation,and conducts an indepth study of the expectation maximization algorithm used in the maximum likelihood estimation of the hidden variable parameters,aiming at the shortcomings of the slow convergence speed of the EM algorithm The improved Atiken algorithm is studied,and the matching process from the problem template to the predicate is completed,which solves the semantic matching problem of the intelligent question answering system to a certain extent.In this paper,the semantic matching process of the question answering system is transformed into the parameter estimation process of the probability generation model using the expectation maximization algorithm,and the improved Atiken algorithm is studied.System tests show that the semantic matching algorithm proposed in this paper is effective for the function implementation of the question answering system.