基于问答网络论坛知识体系的自动问答系统研究
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摘要
随着信息检索技术的发展,互联网上出现了多种类型的搜索服务。其中应用最为广泛的当数Web搜索引擎服务,实现了对海量Web文档的获取、处理、存储和访问,使用户能够在互联网上方便快捷的查找到所需信息,在人们日常生活中发挥了重要作用。但是,随着互联网信息量的增长和搜索引擎技术的成熟,互联网用户已经不满足于单纯基于关键词的搜索服务,而希望通过自然语言描述,表达自己的查询需求,希望搜索服务系统能够理解用户意图,返回恰当的结果。因此,自动问答系统成了互联网用户的下一步渴望。自动问答系统的用户界面类似于搜索引擎,但用户提交的不再是关键词,而是自然语言问句,系统返回的是根据与问题相关程度排序的答案列表。
目前,自动问答领域已经积累了大量的研究成果,包括基于不同语言的、不同数据集的自动问答研究。但是,自动问答还没能像搜索引擎一样,形成产品化的服务。本文旨在为产品化的自动问答互联网服务积累经验,研究基于一种特定的数据集——问答网络论坛数据集,构建自动问答系统的过程;进而在信息检索过程中,通过引入自然语言句法和语义信息、重新定义数据组织形式等措施,研究系统性能的一系列改进方案。主要贡献和创新点包括:
●构建自动问答系统的评测平台。在问答网络论坛数据集上,参照搜索引擎实现原理,基于标引项,采用文本相似度检索模型构建自动问答系统。实验表明:系统性能略优于问答网络论坛自带的“相似问题搜索”功能,将代替该功能,作为本文系统性能改进的评价基准。
●基于依存项的自动问答系统性能改进。提出依存项定义,在标引项基础上引入了自然语言依存句法分析结果,将句法信息引入到信息检索过程中。实验表明:依存项可以有效表达问题的自然语言句法特征,原有的信息检索模型不做任何改变,即可改进自动问答系统性能。
●基于问题分类的自动问答系统性能改进。针对问答网络论坛数据集,提出一套新的自然语言问题分类体系,将自然语言句法和语义信息作为训练特征,训练出足够精确的问题分类器。问题分类结果用于指导答案排序。实验表明:分类器对论坛数据集分类效果良好,类别指导排序明显改进了系统性能。
●基于自然语言知识体系的自动问答系统改进。参考前两种系统改进经验,提出新的数据组织形式:向概念体系添加谓语关联,建立自然语言知识体系。谓语关联由数据集中的问题答案对产生。这是一种综合的改进,既充分利用了数据集中的答案信息,又借助自然语言概念体系的关联关系,增强了系统的查询扩展和逻辑推理能力。论坛数据填充到此体系中,并在此体系上重建自动问答系统。实验表明:重建后系统性能得到全面改进。
With the development of information retrieval technology, various types of search services have appeared on the Internet. In all the services, the one that is most widely used is Web search engine, which has realized the acquisition, processing, storage and access on the mass of Web documents, in order that users can find necessary information on the Internet quickly and easily. Thus the Web search engine plays an important role in people's daily life. However, for the growth of Internet information and the maturity of search technology, Internet users have no longer satisfied with a keyword-based search service, and hope that they can express their query needs through natural language description, and the search service system can understand their intention to return appropriate results. Therefore, the automatic question answering system has become the next desire of Internet users. Automatic question answering system offers a user interface similar to search engines; while users will no longer commit keywords, but natural language questions. The system will return a list of answers ranked by their association with the question.
By present, a great deal of research results has been accumulated in the field of automatic question answering, including those based on different languages and different data sets. However, there is no automatic question answering service yet, as a product as search engine.
Aiming at the accumulation of experience for the product of automatic question answering Internet service, this paper studies the construction process of automatic question answering system, which is based on a specific data set - Question and Answer Web Forum data sets. This paper also studies a series of programs to improve the system performance, through the introduction of natural language syntactic and semantic information and new organizational form of data sets into information retrieval process. The main contributions and innovations include:
·The construction of evaluation platform for automatic question answering system. Build an automatic question answering system on the question and answer forum data sets, referring to the realization of the search engine, using the term-based text similarity model. Experiments show that: the performance of the system is slightly better than that of their own similar-question-search service in question and answer forums. Then it will be treated as the evaluation baseline of system performance improvement in this paper, instead of the similar-question-search service.
·Performance improvement of automatic question answering system based on dependency term. A definition of dependency term is proposed, based on term, integrating the natural language dependency structure, introducing syntactic information into the information retrieval process. Experiments show that: dependency term can effectively express the characteristics of natural language questions, and improves the performance of automatic question answering system without changing of original information retrieval models.
·Performance improvement of automatic question answering system based on question classification. A new definition of taxonomy for natural language questions is proposed, for the the question and answer forum data sets, and a question classifier is trained by natural language syntactic and semantic features, which is accurate enough to guide the answer ranking. Experiments show that: the question classifier works well on the Web forum data sets, and the question-class-guided ranking significantly improves the system performance.
·Performance improvement of automatic question answering system based on natural language-based knowledge system. Madding references to the former two performance improving methods of the system, a new data organizing form is proposed: adding predicate links to concept system to establish a natural language-based knowledge system. The predicate links are generated from the question/answer pairs in the date sets. This is a comprehensive improvement; making full use of not only the information from answers, but also the relationships form concept system, enhancing the logical reasoning ability of the system. Forum data are all filled into the knowledge system, based on which the automatic question answering system is rebuilt. Experiments show that: the reconstruction brings further improvement to the system performance.
目前,自动问答领域已经积累了大量的研究成果,包括基于不同语言的、不同数据集的自动问答研究。但是,自动问答还没能像搜索引擎一样,形成产品化的服务。本文旨在为产品化的自动问答互联网服务积累经验,研究基于一种特定的数据集——问答网络论坛数据集,构建自动问答系统的过程;进而在信息检索过程中,通过引入自然语言句法和语义信息、重新定义数据组织形式等措施,研究系统性能的一系列改进方案。主要贡献和创新点包括:
●构建自动问答系统的评测平台。在问答网络论坛数据集上,参照搜索引擎实现原理,基于标引项,采用文本相似度检索模型构建自动问答系统。实验表明:系统性能略优于问答网络论坛自带的“相似问题搜索”功能,将代替该功能,作为本文系统性能改进的评价基准。
●基于依存项的自动问答系统性能改进。提出依存项定义,在标引项基础上引入了自然语言依存句法分析结果,将句法信息引入到信息检索过程中。实验表明:依存项可以有效表达问题的自然语言句法特征,原有的信息检索模型不做任何改变,即可改进自动问答系统性能。
●基于问题分类的自动问答系统性能改进。针对问答网络论坛数据集,提出一套新的自然语言问题分类体系,将自然语言句法和语义信息作为训练特征,训练出足够精确的问题分类器。问题分类结果用于指导答案排序。实验表明:分类器对论坛数据集分类效果良好,类别指导排序明显改进了系统性能。
●基于自然语言知识体系的自动问答系统改进。参考前两种系统改进经验,提出新的数据组织形式:向概念体系添加谓语关联,建立自然语言知识体系。谓语关联由数据集中的问题答案对产生。这是一种综合的改进,既充分利用了数据集中的答案信息,又借助自然语言概念体系的关联关系,增强了系统的查询扩展和逻辑推理能力。论坛数据填充到此体系中,并在此体系上重建自动问答系统。实验表明:重建后系统性能得到全面改进。
With the development of information retrieval technology, various types of search services have appeared on the Internet. In all the services, the one that is most widely used is Web search engine, which has realized the acquisition, processing, storage and access on the mass of Web documents, in order that users can find necessary information on the Internet quickly and easily. Thus the Web search engine plays an important role in people's daily life. However, for the growth of Internet information and the maturity of search technology, Internet users have no longer satisfied with a keyword-based search service, and hope that they can express their query needs through natural language description, and the search service system can understand their intention to return appropriate results. Therefore, the automatic question answering system has become the next desire of Internet users. Automatic question answering system offers a user interface similar to search engines; while users will no longer commit keywords, but natural language questions. The system will return a list of answers ranked by their association with the question.
By present, a great deal of research results has been accumulated in the field of automatic question answering, including those based on different languages and different data sets. However, there is no automatic question answering service yet, as a product as search engine.
Aiming at the accumulation of experience for the product of automatic question answering Internet service, this paper studies the construction process of automatic question answering system, which is based on a specific data set - Question and Answer Web Forum data sets. This paper also studies a series of programs to improve the system performance, through the introduction of natural language syntactic and semantic information and new organizational form of data sets into information retrieval process. The main contributions and innovations include:
·The construction of evaluation platform for automatic question answering system. Build an automatic question answering system on the question and answer forum data sets, referring to the realization of the search engine, using the term-based text similarity model. Experiments show that: the performance of the system is slightly better than that of their own similar-question-search service in question and answer forums. Then it will be treated as the evaluation baseline of system performance improvement in this paper, instead of the similar-question-search service.
·Performance improvement of automatic question answering system based on dependency term. A definition of dependency term is proposed, based on term, integrating the natural language dependency structure, introducing syntactic information into the information retrieval process. Experiments show that: dependency term can effectively express the characteristics of natural language questions, and improves the performance of automatic question answering system without changing of original information retrieval models.
·Performance improvement of automatic question answering system based on question classification. A new definition of taxonomy for natural language questions is proposed, for the the question and answer forum data sets, and a question classifier is trained by natural language syntactic and semantic features, which is accurate enough to guide the answer ranking. Experiments show that: the question classifier works well on the Web forum data sets, and the question-class-guided ranking significantly improves the system performance.
·Performance improvement of automatic question answering system based on natural language-based knowledge system. Madding references to the former two performance improving methods of the system, a new data organizing form is proposed: adding predicate links to concept system to establish a natural language-based knowledge system. The predicate links are generated from the question/answer pairs in the date sets. This is a comprehensive improvement; making full use of not only the information from answers, but also the relationships form concept system, enhancing the logical reasoning ability of the system. Forum data are all filled into the knowledge system, based on which the automatic question answering system is rebuilt. Experiments show that: the reconstruction brings further improvement to the system performance.
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