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Received:April 26, 2011 Revised:June 05, 2011
Received:April 26, 2011 Revised:June 05, 2011
中文摘要: 问答系统应该能够用准确、简洁的语言回答用户用自然语言提出的问题,其关键和核心实现技术是答案抽取。结合关键词在用户问句和返回文档中的权重,通过潜在语义分析技术实现了中文问答系统中的答案抽取。实验结果表明,加权LSA的MRR值要明显优于未加权LSA和空间向量模型的MRR值,实际用于回答用户提出的问题具有较好的效果。
中文关键词: 问答系统,答案抽取,潜在语义分析,空间向量模型
Abstract:Question answering system returns precise and concise answers for user questions in natural language, and its core technology is answer extraction. Based on weight importance of different keywords in user’ questions and returned documents, a method for computing keyword weight is proposed. In the meantime, the weighted Latent Semantic Analysis technique is also introduced in this process. Experimental results show that the MRR of the proposed method is better than that of Vector Space Model, and gets a more satisfactory performance.
keywords: answer question system answer extracting latent semantic analysis(LSA) vector space model(VSM)
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基金项目:安徽省高校省级自然科学基金(KJ2010B223)
引用文本：
陈永平,杨思春,苏新,毛万胜.基于加权潜在语义分析的答案抽取.计算机系统应用,2012,21(1):40-44
CHEN Yong-Ping,YANG Si-Chun,SU Xin,MAO Wan-Sheng.Answer Extraction Based on Weighted Latent Semantic Analysis.COMPUTER SYSTEMS APPLICATIONS,2012,21(1):40-44
陈永平,杨思春,苏新,毛万胜.基于加权潜在语义分析的答案抽取.计算机系统应用,2012,21(1):40-44
CHEN Yong-Ping,YANG Si-Chun,SU Xin,MAO Wan-Sheng.Answer Extraction Based on Weighted Latent Semantic Analysis.COMPUTER SYSTEMS APPLICATIONS,2012,21(1):40-44
