QA-KGNet: 一种语言模型驱动的知识图谱问答模型

基于知识图谱的问答系统可以解析用户问题,已成为一种检索知识、自动回答所询问题的有效途径.知识图谱问答系统通常是利用神经程序归纳模型,将自然语言问题转化为逻辑形式,在知识图谱上执行该逻辑形式能够得到答案.然而,使用预训练语言模型和知识图谱的知识问答系统包含两个挑战:(1)给定问答(questionanswering, QA)上下文,需要从大型知识图谱(knowledge graph, KG)中识别相关知识;(2)对QA上下文和KG进行联合推理.基于此,提出一种语言模型驱动的知识图谱问答推理模型QA-KGNet,将QA上下文和KG连接起来形成一个工作图,使用语言模型计算给定QA上下文节点与KG节点的关联度,并使用多头图注意力网络更新节点表示.在Commonsense QA、OpenBookQA和Med QA-USMLE真实数据集上进行实验来评估QA-KGNet的性能,实验结果表明:QA-KGNet优于现有的基准模型,表现出优越的结构化推理能力.     

融合语义信息与问题关键信息的多阶段注意力答案选取模型

自动问答系统可以帮助人们快速从海量文本中提取出有效信息,而答案选取作为其中的关键一步,在很大程度上影响着自动问答系统的性能.针对现有答案选择模型中答案关键信息捕获不准确的问题,本文提出了一种融合语义信息与问题关键信息的多阶段注意力答案选取模型.该方法首先利用双向LSTM模型分别对问题和候选答案进行语义表示;然后采用问题的关键信息,包括问题类型和问题中心词,利用注意力机制对候选答案集合进行信息增强,筛选Top K个候选答案;然后采用问题的语义信息,再次利用注意力机制对Top K个候选答案集合进行信息增强,筛选出最佳答案.通过分阶段地将问题的关键信息和语义信息与候选答案的语义表示相结合,有效提高了对候选答案关键信息的捕获能力,从而提升了答案选取系统的性能.在三个数据集上对本文所提出的模型进行验证,相较已知同类最好模型,最高性能提升达1.95%.     

The shaping of information by visual metaphors

The nature of an information visualization can be considered to lie in the visual metaphors it uses to structure information. The process of understanding a visualization therefore involves an interaction between these external visual metaphors and the user's internal knowledge representations. To investigate this claim, we conducted an experiment to test the effects of visual metaphor and verbal metaphor on the understanding of tree visualizations. Participants answered simple data comprehension questions while viewing either a treemap or a node-link diagram. Questions were worded to reflect a verbal metaphor that was either compatible or incompatible with the visualization a participant was using. The results suggest that the visual metaphor indeed affects how a user derives information from a visualization. Additionally, we found that the degree to which a user is affected by the metaphor is strongly correlated with the user's ability to answer task questions correctly. These findings are a first step towards illuminating how visual metaphors shape user understanding, and have significant implications for the evaluation, application, and theory of visualization.     

Development of a rapid prototyping design advice system

This paper describes the initial development of a computer based Rapid Prototyping Design Advice System. The system is intended to assist the designer or project manager, particularly those in small and medium sized companies, in planning the prototyping stage of product development. It provides the user with an efficient and user friendly development aid which uses information obtained from the user and Computer Aided Design data to recommend suitable rapid prototyping solutions.     

SINA: Semantic interpretation of user queries for question answering on interlinked data

The architectural choices underlying Linked Data have led to a compendium of data sources which contain both duplicated and fragmented information on a large number of domains. One way to enable non-experts users to access this data compendium is to provide keyword search frameworks that can capitalize on the inherent characteristics of Linked Data. Developing such systems is challenging for three main reasons. First, resources across different datasets or even within the same dataset can be homonyms. Second, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain user query. Finally, constructing a federated formal query from keywords across different datasets requires exploiting links between the different datasets on both the schema and instance levels. We present Sina, a scalable keyword search system that can answer user queries by transforming user-supplied keywords or natural-languages queries into conjunctive SPARQL queries over a set of interlinked data sources. Sina uses a hidden Markov model to determine the most suitable resources for a user-supplied query from different datasets. Moreover, our framework is able to construct federated queries by using the disambiguated resources and leveraging the link structure underlying the datasets to query. We evaluate Sina over three different datasets. We can answer 25 queries from the QALD-1 correctly. Moreover, we perform as well as the best question answering system from the QALD-3 competition by answering 32 questions correctly while also being able to answer queries on distributed sources. We study the runtime of SINA in its mono-core and parallel implementations and draw preliminary conclusions on the scalability of keyword search on Linked Data.     

基于领域知识的问答对自动提取方法

问答系统旨在用准确、简洁的答案回答用户用自然语言提出的问题。以旅游信息服务为应用背景,提出了基于领域知识的问答对自动提取方法。考察了常见旅游问题,建立了领域知识,在此基础上,设计了用户问题模式匹配算法和答案提取算法,对于不能匹配模式的问题,采用句子相似度计算得到相关的答案。实验结果表明,提出的方法是可行的,实现了旅游问题的自动问答。     

基于问句相似度的中文FAQ问答系统

常见问题(FAQ)问答系统是一种在已有的“问题—答案”对集合中找到与用户提问相匹配的问句,并将其对应的答案返回给用户的问答式检索系统。其关键问题是用户提出问句与FAQ库中问句进行相似度计算,找出FAQ库中最相近的问句,并返回事先存储好的问题答案。通过对常见问句特点的研究,给出一种基于分解的向量空间模型和语义概念的问句相似度计算方法,其主要思想是对一个问句向量进行分解,提取其三个关键部分:问点、主题词和疑问词,表示成三个分向量,然后对每个分向量计算基于《HIT-IRLab同义词词林(扩展版)》的语义相似度,通过线性加权就可以得出两个问句的语义相似度。试验表明,与传统的基于向量空间模型的TF-DF问句相似度计算方法相比,可以提高问句匹配的精度。     

基于网络的中文问答系统的研究

文章设计并实现了一个基于网络的中文问答系统。该系统只利用网络搜索引擎返回结果中的摘要部分作为答案抽取的资源,从而节省了下载、分析网络源文本的时间,提出了一种针对该系统的信息抽取算法,并采用一种基于语句相似度计算的答案抽取算法并且进行了改进。实验结果表明该系统对人名及时间类型的问题效果显著。对测试问题集的MRR值达到0.47。     

Generation of efficient and user-friendly queries for helper robots to detect target objects

We are developing a helper robot that carries out tasks ordered by users through speech. The robot needs a vision system to recognize the objects appearing in the orders. However, conventional vision systems cannot recognize objects in complex scenes. They may find many objects and cannot determine which is the target. This paper proposes a method of using a conversation with the user to solve this problem. The robot asks a question to which the user can easily answer and whose answer can efficiently reduce the number of candidate objects. It considers the characteristics of features used for object identification such as the ease for humans to specify them by word, generating a user-friendly and efficient sequence of questions. Experimental results show that the robot can detect target objects by asking the questions generated by the method.     

Intelligent agents and liability: is it a doctrinal problem or merely a problem of explanation?

The question of liability in the case of using intelligent agents is far from simple, and cannot sufficiently be answered by deeming the human user as being automatically responsible for all actions and mistakes of his agent. Therefore, this paper is specifically concerned with the significant difficulties which might arise in this regard especially if the technology behind software agents evolves, or is commonly used on a larger scale. Furthermore, this paper contemplates whether or not it is possible to share the responsibility with these agents and what are the main objections surrounding the assumption of considering such agents as responsible entities. This paper, however, is not intended to provide the final answer to all questions and challenges in this regard, but to identify the main components, and provide some perspectives on how to deal with such issue.     

基于招生领域自动问答系统的问题理解的研究

问题理解是问答系统的首要的分析工作,分析的结果对后面的处理,以至找到问题的正确答案都有很大的影响。该文将对常规的问题理解方法进行改进,从而使系统能够较准确地回答用户的提问。实验证明新的方法对提高系统性能有显著作用,尤其针对性强、意思表述清晰的提问,回答准确率有很大提高。     

基于招生领域自动问答系统的问题理解的研究

问题理解是问答系统的首要的分析工作,分析的结果对后面的处理,以至找到问题的正确答案都有很大的影响。该文将对常规的问题理解方法进行改进,从而使系统能够较准确地回答用户的提问。实验证明新的方法对提高系统性能有显著作用,尤其针对性强、意思表述清晰的提问,回答准确率有很大提高。     

自动问答系统中的问题理解研究

问题理解是问答系统的首要的分析工作,分析的结果对后面的处理,以至找到问题的正确答案都有很大的影响。本文将对常规的问题理解方法进行改进,从而使系统能够较准确地回答用户的提问。实验证明新的方法对提高系统性能有显著作用,尤其针对性强、意思表述清晰的提问,回答准确率有很大提高。     

基于JSP和Android的C/S结构问卷系统

为了方便问卷调查的实施过程,提高统计数据采集过程的效率,提出一种基于Android平台的手机问卷调查系统实现方法。该问卷系统是客户端/服务器结构的,服务端包括基于JSP的问卷设计模块、问卷信息统计模块、数据库,以及基于C#的Web Service用于实现数据库访问接口; 客户端是Android平台上的问卷交互系统,从数据库获取问卷信息,在手机上显示问题和选项供用户回答并把回答结果提交到数据库。该系统在Android模拟器上以及真机上都进行了测试,结果表明该系统能有效实现问卷调查功能。由于客户端可以运行在移动设备之上,该系统的实现可以使问卷过程随时随地进行,使调查过程更加便利和高效,拓宽问卷调查的客户群,因此可以为需要市场调查或社会调研的企业或机构所采用。     

汉语句子相似度计算在FAQ中的应用

通过对传统的汉语句子相似度模型进行改进,提出一种基于关键词加权的汉语句子相似度计算方法,在此基础上实现一个基于常问问题库的中文问答系统。该系统通过将用户输入的自然语言问句与常问问题库中的候选问题集进行相似度计算,自动返回最匹配的答案给用户,自动更新和维护常问问题库。实验结果表明该方法在问旬匹配上比传统方法具有更高的准确率。     

Identifying Predictive Metrics for Supervisory Control of Multiple Robots

In recent years, much research has focused on making possible single-operator control of multiple robots. In these high workload situations, many questions arise including how many robots should be in the team, which autonomy levels should they employ, and when should these autonomy levels change? To answer these questions, sets of metric classes should be identified that capture these aspects of the human-robot team. Such a set of metric classes should have three properties. First, it should contain the key performance parameters of the system. Second, it should identify the limitations of the agents in the system. Third, it should have predictive power. In this paper, we decompose a human-robot team consisting of a single human and multiple robots in an effort to identify such a set of metric classes. We assess the ability of this set of metric classes to: 1) predict the number of robots that should be in the team and 2) predict system effectiveness. We do so by comparing predictions with actual data from a user study, which is also described.     

支持向量机算法在MOOC 课程答疑系统中的研究

随着互联网技术和近期MOOC课程的发展,智能答疑系统也受到了更多的关注,应用它能够及时给学生提供学生疑惑的问题答案。智能答疑系统通常包括问句理解、信息检索、答案抽取和选择三个主要部分,且问句分类是问句理解的关键,因为它的准确性将直接影响到最后答案的准确性。以高校计算机基础课程为实际背景,在已有基于支持向量机算法基础上,对该方法进行了改进,并通过训练集和测试集进行了验证。从实验结果看,该方法在高校计算机基础智能答疑系统中有比较好的应用效果。     

基于向量空间模型的问答题智能化评分系统

网络考试系统中问答题自动评分一直是一个技术难点。为了突破该技术难点,设计并实现了一种基于向量空间模型(VSM)的问答题智能化评分系统。该系统首先依据VSM将学生的答案及标准答案表示成特征向量的形式,然后计算学生的答案与标准答案的相似程度,从而确定学生的得分。该系统采用J2EE技术实现,并已成功应用到了网络考试系统中。     

句子相似度计算及其应用

随着互联网技术的发展,智能答疑系统也受到了更多的关注,应用它能够及时给学生提供学生疑惑的问题答案。智能答疑系统通常包括问句理解、信息检索、答案抽取和选择三个主要部分,其中句子相似度计算是问句理解的一部分,它的性能将直接影响到最后答案的准确性。本文通过对词型和普通的编辑距离算法为基础,加入了词性的语义信息,提出了一种新的句子相似度算法,并将其应用到计算机基础课程答疑系统中,使得系统的正确率有了较大的提高。