Research On Network Intelligent Question Answer System Model

With the appearance and development of network technology,Distance Education based on network education develops rapidly as a new teaching mode.It breaks the time and space limitation and provides convenient learning ways and diversiform learning environment.But in this education mode,teachers and students are separated from each other and can not communicate face-to-face and the students' problems can not be answered in time.So establishing an intelligent Question Answer System Model is one of important tasks of development of network education system.As one important part of network education,Question Answer System plays an important role at helping students rescind their doubts,knowledge acquisition and enhancing mutual interaction between teachers and students.However,the traditional Question Answer System cannot realize semantic matching of questions and answers and cannot not fill the require of increasing network educational needs because it is based on keyword matching mostly and short of semantic understanding.On the basis of research on the theories and technologies related to automatic Question Answering,the thesis analyzes the key technologies of Network Intelligent Question Answer.To solve the problem of segmentation inaccuracy of user question sentences,the paper puts forward the reverse maximum matching word segmentation algorithm based on concept dictionary and common dictionary improving the accuracy of segmentation.In view of the matching degree of question vector after marking and question of Frequently Asked Questions,the paper proposes a mechanism of keywords standardization making the matching degree as high as possible,avoiding the latter complex similarity computation and increasing the Retrieval speed.According to the problem of excessive noise information after keywords extension,the thesis proposes a mechanism of classified keywords expansion avoiding drift of retrieval theme and giving a hint to user for heuristic search.Aiming at different effects of keywords in retrieval are not considered in the traditional vector space model,the paper presents a question similarity computation approach based on splited vector space model and semantic concepts raising the accuracy of similarity computation.Based on the above key techniques,the article proposes a new Network Intelligent Question Answer System Model and analyzes each packet-handling module and gives the processing flow of the system model.The system model realizes semantic understanding of Question Answer system improving efficiency of the present Question Answer system and with certain intelligence.