To solve the problem of the inadequacy of semantic processing in the intelligent question answering system, an integrated semantic similarity model which calculates the semantic similarity using the geometric distance...To solve the problem of the inadequacy of semantic processing in the intelligent question answering system, an integrated semantic similarity model which calculates the semantic similarity using the geometric distance and information content is presented in this paper. With the help of interrelationship between concepts, the information content of concepts and the strength of the edges in the ontology network, we can calculate the semantic similarity between two concepts and provide information for the further calculation of the semantic similarity between user’s question and answers in knowledge base. The results of the experiments on the prototype have shown that the semantic problem in natural language processing can also be solved with the help of the knowledge and the abundant semantic information in ontology. More than 90% accuracy with less than 50 ms average searching time in the intelligent question answering prototype system based on ontology has been reached. The result is very satisfied. Key words intelligent question answering system - ontology - semantic similarity - geometric distance - information content CLC number TP39 Foundation item: Supported by the important science and technology item of China of “The 10th Five-year Plan” (2001BA101A05-04)Biography: LIU Ya-jun (1953-), female, Associate professor, research direction: software engineering, information processing, data-base application.展开更多
问答系统在MOOC(Massive Open Online Course)学习平台中占有很重要的地位,但由于MOOC课程学习中用户多、问题多的特点,快速准确地找到某一问题的答案,成为提升MOOC平台用户体验的一个挑战。为此,构建了面向常问问题集的问答系统,通过...问答系统在MOOC(Massive Open Online Course)学习平台中占有很重要的地位,但由于MOOC课程学习中用户多、问题多的特点,快速准确地找到某一问题的答案,成为提升MOOC平台用户体验的一个挑战。为此,构建了面向常问问题集的问答系统,通过计算问句的相似性实现系统的自动问答。在现有的句子相似性度量方法基础上,设计了基于语义依存关系的度量方法,并结合了句长、词形、词义等多种特征度量句子之间的相似性。实验结果表明,该方法能够较好地反映句子之间的语义差别,是一种可行有效的方法,且提高了系统的响应准确率。该系统具有较高应答准确率,具有较强使用价值和广阔的应用前景。展开更多
文摘To solve the problem of the inadequacy of semantic processing in the intelligent question answering system, an integrated semantic similarity model which calculates the semantic similarity using the geometric distance and information content is presented in this paper. With the help of interrelationship between concepts, the information content of concepts and the strength of the edges in the ontology network, we can calculate the semantic similarity between two concepts and provide information for the further calculation of the semantic similarity between user’s question and answers in knowledge base. The results of the experiments on the prototype have shown that the semantic problem in natural language processing can also be solved with the help of the knowledge and the abundant semantic information in ontology. More than 90% accuracy with less than 50 ms average searching time in the intelligent question answering prototype system based on ontology has been reached. The result is very satisfied. Key words intelligent question answering system - ontology - semantic similarity - geometric distance - information content CLC number TP39 Foundation item: Supported by the important science and technology item of China of “The 10th Five-year Plan” (2001BA101A05-04)Biography: LIU Ya-jun (1953-), female, Associate professor, research direction: software engineering, information processing, data-base application.
文摘问答系统在MOOC(Massive Open Online Course)学习平台中占有很重要的地位,但由于MOOC课程学习中用户多、问题多的特点,快速准确地找到某一问题的答案,成为提升MOOC平台用户体验的一个挑战。为此,构建了面向常问问题集的问答系统,通过计算问句的相似性实现系统的自动问答。在现有的句子相似性度量方法基础上,设计了基于语义依存关系的度量方法,并结合了句长、词形、词义等多种特征度量句子之间的相似性。实验结果表明,该方法能够较好地反映句子之间的语义差别,是一种可行有效的方法,且提高了系统的响应准确率。该系统具有较高应答准确率,具有较强使用价值和广阔的应用前景。
