受限领域问答系统的中文问句分析研究

对用户所提问句的理解是受限领域问答系统实现的关键,该文提出一种基于本体和问句句型模板规则的中文问句分析方法,研究如何使用问句语义表征来表示问句分析的结果,将该方法应用于某受限领域问答系统中。实验结果表明,使用该方法进行中文问句分析,准确率达90%以上,可以在实际的问答系统中使用该方法。     

基于Web的受限领域问答系统研究及应用

自动问答技术是自然语言处理领域中一个非常热门的研究方向,它综合运用了自然语言处理技术,传统的问答系统的知识库一般是基于固定的文档集合,丰富的网络资源为问答系统提供了另外一种良好的知识来源,提出并构建了一个基于网络的天气预报领域的问答系统,通过缩小问题的覆盖面的方法,提高了系统的准确性。     

中文问答系统中的问句理解

描述了一种通过人工总结规则建立模型来理解问答系统中问句的方法。该模型采用浅层语法分析和部分语义分析相结合的方法来断定一个问句所包含的信息。     

查询扩展在开放领域问答系统中的应用

文档检索是问答式检索系统的重要组成部分,它提供了整个系统进行答案抽取等后续处理的基础.查询扩展是一种能有效地改进文档检索性能的常用方法.FDUQA在检索模块中结合自然语言处理技术,引入了查询扩展功能.实验证明,通过在开放领域问答系统的检索模块中加入查询扩展,文档检索的首位正确率提高了11.6%,返回文档集的整体质量有了明显的提高,提高了系统的整体性能.     

受限领域中文问答系统的中心句识别的研究

在自动问答系统中,只有识别用户输入问句的具体含义后,才能做出相应的处理。文章尝试使用领域知识库作为基础,运用模式匹配的方法实现对用户输入问句的分类,识别问句中的中心句和关键词,计算这些关键词的概念与领域知＋识库中已有概念的相似度,并将这些参数传给检索系统采检出合适的答案。     

基于关键词距离的中文问答系统研究

阐述的中文问答系统是以网络信息为支撑,结合传统问答系统的思想和网络信息资源的特点,采取切实有效的方法,来回答用户用自然语言形式提出的问题.系统的核心部分是依据关键词距离算法进行答案抽取,该算法是在总结大规模网络摘要及中文问句特点的基础上得出的,从最后实验结果看该系统效果显著,对测试问句集的MRR值达到了0.56.     

问答系统研究综述

问答系统是人工智能和自然语言处理领域中具有广泛发展前景的研究方向之一.早期的问答系统限定以自然语言形式进行提问和回答,近年来,随着多模态知识图谱、多模态预训练模型的发展,支持文字、图片、音频、视频等多种模态间信息查询的广义问答系统逐渐成为新的研究热点,其以多媒体方式展示结果,更加直观、全面.本文根据问答系统任务对象的变化,将问答系统划分为3种类型:专用问答系统、通用问答系统和多模态问答系统.分析了这3种类型的问答系统发展过程中所面临的问题,着重总结每个阶段所采用的关键技术与方法,同时对问答系统在工业上的应用进行了举例说明,并对未来研究方向进行了展望.     

中文自动问答系统探讨

中文自动问答系统是网络高速发展的必然产物。论文从自动问答系统的定义入手,系统的研究了它的关键技术,并对其应用前景和发展趋势做了预测。     

问答系统中基于维基百科的问题扩展技术研究

为了解决文档与查询之间词的不匹配的问题,对问题扩展技术进行了研究,提出了一种基于维基百科的查询扩展方法.该方法使用与问题相关的维基百科页面对问题扩展,引入了基于局部文档集的查询扩展方法,并使用BM25算法对检索排序进行修正.通过测评对比,验证了用此方法得到的检索结果在原来的基础上有了很大提高.     

基于自动问答系统的信息检索技术研究进展

自动问答是根据用户以自然语言提出的问题给出一个明确的答案。近年来,自动问答越来越受到信息检索和自然语言处理的研究者的关注。典型的自动问答系统通常包含问题分析、文段检索和答案选择等部件。介绍了自动问答的最新研究进展和相关国际会议情况,着重阐述问题分类、查询扩展、文段检索和答案选择这四个热点技术的主要功能和常用方法,最后提出存在的一些问题和展望。     

Skeleton parsing for complex question answering over knowledge bases

Answering complex questions involving multiple relations over knowledge bases is a challenging task. Many previous works rely on dependency parsing. However, errors in dependency parsing would influence their performance, in particular for long complex questions. In this paper, we propose a novel skeleton grammar to represent the high-level structure of a complex question. This lightweight formalism and its BERT-based parsing algorithm help to improve the downstream dependency parsing. To show the effectiveness of skeleton, we develop two question answering approaches: skeleton-based semantic parsing (called SSP) and skeleton-based information retrieval (called SIR). In SSP, skeleton helps to improve structured query generation. In SIR, skeleton helps to improve path ranking. Experimental results show that, thanks to skeletons, our approaches achieve state-of-the-art results on three datasets: LC-QuAD 1.0, GraphQuestions, and ComplexWebQuestions 1.1.     

中文问答系统中机构名的处理

探讨问句中机构名的处理,并服务于中文问答系统。采用概念层次网络理论的语义概念分析方法分析问句,去掉与机构名无关的概念词语,得到候选机构名。对机构名全称库按字索引,在库中搜索出与候选机构名匹配的机构名全称并按拟合权值排序。实验结果表明该方法识别机构名的正确率达到90.6%,支持对机构名简称的处理。     

Arabic question answering system: a survey

Question answering is a subfield of information retrieval. It is a task of answering a question posted in a natural language. A question answering system (QAS) may be considered a good alternative to search engines that return a set of related documents. The QAS system is composed of three main modules; question analysis, passage retrieval, and answer extraction. Over the years, numerous QASs have been presented for use in different languages. However, the the development of Arabic QASs has been slowed by linguistic challenges and the lack of resources and tools available to researchers. In this survey, we start with the challenges due to the language and how these challenges make the development of new Arabic QAS more difficult. Next, we do a detailed review of several Arabic QASs. This is followed by an in-depth analysis of the techniques and approaches in the three modules of a QAS. We present an overview of important and recent tools that were developed to help the researchers in this field. We also cover the available Arabic and multilingual datasets, and a look at the different measures used to assess QASs. Finally, the survey delves into the future direction of Arabic QAS systems based on the current state-of-the-art techniques developed for question answering in other languages.

     

A fuzzy reasoning database question answering system

Describes a question-answering system based on fuzzy logic. The proposed system provides the capability to assess whether a database contains information pertinent to a subject of interest by evaluating each comment in the database via a fuzzy evaluator that attributes a fuzzy membership value indicating its relationship to the subject. An assessment is provided for the database as a whole regarding its pertinence to the subject of interest, and consequently comments that are considered irrelevant to the subject may be discarded. The system has been developed for the examination of databases that were created during the development of the IBM 4381 computer systems, for bookkeeping purposes, to assess whether such databases contain information pertinent to the functional changes that occurred during the development cycle. The system, however, can be applied with minimal changes to a variety of circumstances, provided that the fundamental assumptions for the development of the membership functions are respected in the new application. Its applicability, without modifications, assuming the same subject of interest, is granted for databases comprising similar characteristics to that of the original database for which the system has been developed     

智能答疑中问题相关度算法研究及系统实现

针对现有答疑系统缺乏智能性和人机交互不够友好的不足,提出了一个智能答疑系统实现方案。为提高系统中问题与答案的匹配准确程度,着重对问题相关度算法进行了研究,在自动分词后用关键词集合相似度来计算问题的相关度,通过有监督的机器学习BP模型建立一个适合智能答疑系统的学习模型来优化分词权值。实验证明,这种算法可以帮助智能答疑系统提高准确性和智能性,具有一定的实用价值。     

网络答疑本体的生成与匹配方法研究*

针对网络答疑具体任务,提出答疑本体概念,构建答疑本体,并提出一种基于答疑本体的答案匹配方法和相似度计算公式。公式不仅考虑词之间的相似度,还考虑问题答案对(QAp)在本体中的位置。该位置信息隐含了句子结构上的语义和词的语义扩展。实验结果表明提出的答疑本体能够有效表示QAp,易于答疑系统的检索匹配。     

基于HNC句类的社区问答系统问句检索模型构建

社区问答系统中充斥着大量的噪声,给用户检索信息造成麻烦,以往的问句检索模型大多集中在词语层面。针对以上问题构建句子层面的问句检索模型。新模型基于概念层次网络（hierarchincal network of concept,HNC）理论当中的句类知识,从句子的语用、语法和语义三个层面计算问句间相似度。通过问句分类算法确定查询问句和候选问句的问句类别,得到问句间的语用相似度,利用句类表达式的结构和语义块组成分别计算问句间的语法及语义相似度。在真实数据集上的实验表明,基于HNC句类的新模型提高了问句检索结果的准确性。     

基于多特征语义匹配的知识库问答系统

知识库问答(KBQA)任务主要目的在于精确地将自然语言问题和知识库(KB)中的三元组进行匹配。传统的KBQA方法通常专注于实体识别和谓语匹配,实体识别的错误会导致错误传播从而无法得到正确的答案。针对上述问题提出一种端到端的解决方案直接匹配问题和三元组,该系统主要包含候选三元组生成和候选三元组排序两个部分来实现精确问答。首先通过BM25算法计算问题和知识库中三元组的相关性生成候选三元组;然后通过多特征语义匹配模型(MFSMM)进行三元组的排序,即用MFSMM分别通过双向长短时记忆网络(Bi-LSTM)和卷积神经网络(CNN)实现语义相似度和字符相似度的计算,并通过融合来对三元组进行排序。该系统在NLPCC-ICCPOL 2016KBQA数据集上的平均F1为80. 35%,接近了现有最好的表现。     

A question answering system for project management applications

The usage of computer applications in the construction industry is increasing, as is the complexity of software applications and this makes it difficult for project personnel to maintain familiarity. Furthermore, the causes of practical problems, such as project delays and cost over-runs, are often not derivable from the output of most software. A question answering system provides a means for directly extracting knowledge from this output. This paper begins with an examination of issues involved in building such a system. An emerging industry standard, ifcXML, is adopted as the knowledge representation format, thereby reducing the effort that is necessary to build a knowledge base. We then explore the mechanisms that use information in the knowledge base for question understanding. A prototype system has been built and tested to illustrate usefulness for project management applications.