A Question Understanding Model Based on Knowledge Points for Chinese Question Answering Service in E-Learning

Authors:
Zheng-Hong Wu;Ming Li;Huamin Feng
Affiliations:
School of Information Science & Technology, East China Normal University, Shanghai 200062, P.R. China;School of Information Science & Technology, East China Normal University, Shanghai 200062, P.R. China;Key Laboratory of Security and Secrecy of Information, Beijing Electronic Science and Technology Institute, Beijing 100070, P.R. China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Year:
2007

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Abstract

Question answering service in e-learning environment is an important issue. Chinese semantic analysis is a key bottleneck for question answering service to understand question's content. This paper proposes a question understanding model to understand and process syntactic and semantic structure. In this paper, we analyzed a lot of questions from students, and clustered questions based on knowledge points. The question understanding model is made to get question focus and question type. According to question focus and question type, the question answering service can precisely know the question's answer by locating knowledge point's attribute. This method could more perfectly understand semantic content of questions than using pure Chinese semantic analysis. It is very useful for students to study in a self-learning environment.