受限域中文问答系统问句分析研究
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摘要
问答系统是新一代智能搜索引擎,它允许用户以自然语言提问,并能够向用户返回准确的答案。与传统的搜索引擎相比,问答系统能更好的满足用户的查询要求,更准确地检索出用户所需要的答案。问句分析是问答系统的一个非常重要的组成部分,它的准确性直接影响到最终答案抽取的准确性。本文主要利用自然语言处理技术,对受限域中领域知识库的构建,问句表征,问句分类,问句相似度计算等问答系统关键技术进行探讨与研究,并在此基础上实现了一个云南旅游FAQ库的问答系统。具体说来,本文主要取得了以下几个较有特色的成果:
(1)针对“知网”常识库缺乏对领域概念的描述问题,提出了一种领域知识的表示、领域本体的提取与构建方法。该方法借助本体论的思想,采用“知网”的概念描述语言对领域术语概念进行精确描述,从而建立领域知识库,并实现领域知识库与常识库的融合。
(2)提出了一种问句信息的形式化表示(问句表征)方法。该方法利用词法、语义分析实现领域问句关键字的提取与扩展,利用问句句法依存分析提取问句的句法依存树,通过问句类型与问点及答案类型映射规则来获取问句的问点及答案类型。
(3)提出了一种基于规则和统计学习相结合的领域问句分类的方法。该方法首先利用语言规则与领域知识的特点提取问句类别规则;然后通过提取句法结构关系和领域特征,并采用改进贝叶斯分类学习算法,构造问句分类模型;最终结合规则的方法和统计学习的方法,实现领域问句分类,实验结果表明,该方法具有较好的效果。
(4)针对当前问句相似度计算方法的不足,结合领域内汉语问句的特点,提出了一种领域问句相似度的计算方法。该方法以领域知识库及常识库为基础,计算词语之间语义相似度,提取问句句法依存对,并计算问句中依存对之间的相似度,从而实现了融合词法、句法、语义及领域知识的领域问句的相似度计算。实验结果表明,该方法具有较好的效果。
(5)利用上述研究成果,并以云南旅游领域为例,收集整理领域特征,实现云南旅游FAQ库的问答原型系统。
Question answering system, as the new generation of intelligent search engine, allows users to ask questions by means of natural language, and can supplly more accurate answers compared with traditional search engines. Question analysis is a very important component of question answering system. The accuracy of question analysis directly affects the accuracy of the ultimate answer extraction. In the paper, Yunnan Tourism FAQ question answering system model is constructed based on natural language processing technology, domain knowledge base construction technology, question formal expression method, question classification and questions similarity calculation.Main distinctive achievements are as follows:
(1) A domain knowledge expression, domain ontology extraction and construction method is proposed according to the deficiency of commonsense knowledge base, HowNet, in domain problem describing. The method is ontology oriented and can construct domain knowledge base, integrate domain knowledge base and common knowledge base on description of domain concept provided by HowNet's concept description language.
(2) A question formal expression method is proposed. The method realized key words extraction and domain question expansion by lexical and semantic analysis. Syntactic interdependence tree of the question is extracted by question syntactic analysis. Question type, question focus and answer type of the question is obtained by the mapping rules of question type and answer type.
(3)A domain question classification method based on language rule and statistical learning is put forward. First, question classification rules are extracted by language rules and domain knowledge. Then, the question classification model is constructed through extracting syntactic structure relation, domain features and improved Bayes classification learning algorithm. At last, domain question classification is realized by combining language rules and statistical learning. Experiment shows the proposed method is feasible.
(4)According to the deficiency of current question similarity method, a domain question similarity calculation method combined with the feature of domain Chinese question is put forward. The method calculates the Semantic similarity between words, extracts question syntactic interdependence pairs, and calculates the similarity between question syntactic interdependence pairs based on domain knowledge base and common knowledge base to calculate domain question similarity which combines lexical, syntactic, semantic and domain knowledge. Experiment result shows great efficiency.
(5)Collect domain features and implement the Yunnan tourism FAQ question answering system based on the research above.
(1)针对“知网”常识库缺乏对领域概念的描述问题,提出了一种领域知识的表示、领域本体的提取与构建方法。该方法借助本体论的思想,采用“知网”的概念描述语言对领域术语概念进行精确描述,从而建立领域知识库,并实现领域知识库与常识库的融合。
(2)提出了一种问句信息的形式化表示(问句表征)方法。该方法利用词法、语义分析实现领域问句关键字的提取与扩展,利用问句句法依存分析提取问句的句法依存树,通过问句类型与问点及答案类型映射规则来获取问句的问点及答案类型。
(3)提出了一种基于规则和统计学习相结合的领域问句分类的方法。该方法首先利用语言规则与领域知识的特点提取问句类别规则;然后通过提取句法结构关系和领域特征,并采用改进贝叶斯分类学习算法,构造问句分类模型;最终结合规则的方法和统计学习的方法,实现领域问句分类,实验结果表明,该方法具有较好的效果。
(4)针对当前问句相似度计算方法的不足,结合领域内汉语问句的特点,提出了一种领域问句相似度的计算方法。该方法以领域知识库及常识库为基础,计算词语之间语义相似度,提取问句句法依存对,并计算问句中依存对之间的相似度,从而实现了融合词法、句法、语义及领域知识的领域问句的相似度计算。实验结果表明,该方法具有较好的效果。
(5)利用上述研究成果,并以云南旅游领域为例,收集整理领域特征,实现云南旅游FAQ库的问答原型系统。
Question answering system, as the new generation of intelligent search engine, allows users to ask questions by means of natural language, and can supplly more accurate answers compared with traditional search engines. Question analysis is a very important component of question answering system. The accuracy of question analysis directly affects the accuracy of the ultimate answer extraction. In the paper, Yunnan Tourism FAQ question answering system model is constructed based on natural language processing technology, domain knowledge base construction technology, question formal expression method, question classification and questions similarity calculation.Main distinctive achievements are as follows:
(1) A domain knowledge expression, domain ontology extraction and construction method is proposed according to the deficiency of commonsense knowledge base, HowNet, in domain problem describing. The method is ontology oriented and can construct domain knowledge base, integrate domain knowledge base and common knowledge base on description of domain concept provided by HowNet's concept description language.
(2) A question formal expression method is proposed. The method realized key words extraction and domain question expansion by lexical and semantic analysis. Syntactic interdependence tree of the question is extracted by question syntactic analysis. Question type, question focus and answer type of the question is obtained by the mapping rules of question type and answer type.
(3)A domain question classification method based on language rule and statistical learning is put forward. First, question classification rules are extracted by language rules and domain knowledge. Then, the question classification model is constructed through extracting syntactic structure relation, domain features and improved Bayes classification learning algorithm. At last, domain question classification is realized by combining language rules and statistical learning. Experiment shows the proposed method is feasible.
(4)According to the deficiency of current question similarity method, a domain question similarity calculation method combined with the feature of domain Chinese question is put forward. The method calculates the Semantic similarity between words, extracts question syntactic interdependence pairs, and calculates the similarity between question syntactic interdependence pairs based on domain knowledge base and common knowledge base to calculate domain question similarity which combines lexical, syntactic, semantic and domain knowledge. Experiment result shows great efficiency.
(5)Collect domain features and implement the Yunnan tourism FAQ question answering system based on the research above.
引文
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[9]E.Nyberg, T.Mitamura, et al. The JAVELIN question answering system at TREC2002[C]. Proceedings of the 11th Text REtrieval Conference, Gaithersburg, NIST, 2002.
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[12]Ichiro Kobayashi.A study on meaning processing of dialogue with an example of development of travel consultation system[J].Information Sciences,2002(144):45-74.
[13]王树西,刘群,白硕.一个人物关系问答系统的专家系统[J].广西师范大学学报,2003,21(1):31-36.
[14]黄寅飞,郑方,燕鹏举.校园导航系统EasyNav的设计与实现[J].中文信息学报,2001,15(4):35-40.
[15]夏天,樊孝忠,刘林,骆正华.基于ALICE的中文自然语言接口[J].北京理工大学学报,2004,24(10):885-889
[16]樊孝忠,李宏乔,李良富.银行领域中文自动问答系统BAQS的研究与实现[J].北京理工大学学报,2004,24(6):528-532.
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[18]梅家驹,等.同义词词林[M].上海:上海辞书出版社出版,1983.
[19]Fellbaum.WordNet[EB/OL].http://www.cogsci.pinceton.edu/～wn/,2002-05.
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[22]Xin Li,Dan Roth.The Role of Semantic Information in Learning Question Classifiers[A].In:First Inernational Joint Conference on Natural Language Processing[C],2004,451-458.
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