计算机应用与软件2016，Vol.33Issue(5)：84-86,119,4.DOI:10.3969/j.issn.1000-386x.2016.05.021
基于HM-SVMs的问句语义分析模型
A QUESTION SEMANTIC ANALYSIS MODEL BASED ON HM-SVMs
摘要
Abstract
Traditional question semantic analysis mainly focus on simple questions in regard to category of facts,but lacks effective semantic analysis method for open field-oriented complex questions.In view of this,we present a new question semantic analysis model.The model maps questions from text space onto a structured semantic space,and achieves semantic analysis and expression of questions.By annotating semantic information in questions the model implements three kinds of analysis works of questions classification,question topic identification and restrictive information identification.We employ hidden Markov support vector machines (HM-SVMs),a serialisation annotation tool,to realise the automatic annotation of the model,and reaches an accuracy of 86.7%.Experimental results show that HM-SVMs is better than MEMM,CRF,M3N and other models in annotation accuracy and efficiency,and achieves the desired effect.关键词
问答系统/问句语义分析/隐马尔科夫支持向量机Key words
Q&A system/Semantic analysis of question/HM-SVMs分类
信息技术与安全科学引用本文复制引用
范士喜,韩喜双,相洋,陈毅..基于HM-SVMs的问句语义分析模型[J].计算机应用与软件,2016,33(5):84-86,119,4.基金项目
广东省教育科学规划教育信息技术研究专项课题（11 JXN039）。范士喜，助理研究员，主研领域问答系统。 （）
