{"title":"Key Techniques of Automatic Question-Answering Customer Service System in College Informatization Domain","authors":"Ching-Chang Wu, Yuxi Chen, Ying Xiong, Junqing Yu","doi":"10.1145/3436756.3437034","DOIUrl":null,"url":null,"abstract":"To improve the efficiency of customer service for college information, an automatic question-answering customer service system was designed and implemented. It could collect the domain-specific corpus was collected in combination with the general corpus, train word vectors, represent the semantics of words, and implement the question similarity calculation algorithm based on word co-occurrence and syntactic analysis to achieve question matching. To minimize the range of question matching and thus make the matching more efficient, a question classification module based on support vector machine (SVM) was implemented. Finally, the validity and usability of the proposed method were verified by experiments. The proposed method shows a certain universality. Given that some colleges have not established their automatic question-answering customer service system, this paper is of great research significance.","PeriodicalId":250546,"journal":{"name":"Proceedings of the 12th International Conference on Education Technology and Computers","volume":"45 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 12th International Conference on Education Technology and Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3436756.3437034","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
To improve the efficiency of customer service for college information, an automatic question-answering customer service system was designed and implemented. It could collect the domain-specific corpus was collected in combination with the general corpus, train word vectors, represent the semantics of words, and implement the question similarity calculation algorithm based on word co-occurrence and syntactic analysis to achieve question matching. To minimize the range of question matching and thus make the matching more efficient, a question classification module based on support vector machine (SVM) was implemented. Finally, the validity and usability of the proposed method were verified by experiments. The proposed method shows a certain universality. Given that some colleges have not established their automatic question-answering customer service system, this paper is of great research significance.