基于自然语言处理的计算机几何作图

如何将自然语言表述的初等几何命题自动转化为计算机可理解的作图语言是自然语言处理中的空白,也是实现教育软件人机交互的难点。文中通过对几何范围内的受限自然语言的研究,建立了有效可行的语言理解模型,实现了从自然语言到形式化规则的自动转化,并且设计出相应的软件。     

Approaches to natural language discourse processing

One of the most difficult problems within the field of Artificial Intelligence (AI) is that of processing language by computer, or natural-language processing. A major problem in natural-language processing is to build theories and models of how individual utterances cling together into a coherent discourse. The problem is important because, to properly understand natural language, a computer should have some sense of what it means for a discourse to be coherent and rational. Theories, models and implementations of natural-language processing argue for a measure of coherence based on three themes: meaning, structure, and intention. Most approaches stress one theme over all the others. Their future lies in the integration of components of all approaches. A theory of intention analysis solves, in part, the problem of natural-language dialogue processing. A central principle of the theory is that coherence of natural-language dialogue can be modelled by analysing sequences of intention. The theory of intention analysis has been incorporated within a computational model, called Operating System CONsultant (OSCON), implemented in Quintus Prolog, which understands, and answers in English, English questions about computer operating systems. Theories and implementations of discourse processing will not only enable people to communicate better with computers, but also enable computers to better communicate with people.     

基于转换的汉语基本名词短语识别模型

基本名词短语的识别在自然语言信息处理领域具有重要作用。本文首先从语言学的角度提出了汉语基本名词短语的概念,然后从语言信息处理的角度将用于基本名词短语识别的知识分为两部分,即表示基本名词短语句法组成的基本结构模板(静态知识)与表示基本名词短语出现的上下文环境特征的转换规则(动态知识)。在此基础上设计了一种基于转换的基本名词短语识别模型,该模型可同时结合这两类知识识别基本名词短语。实验结果显示了较高的识别正确率。     

Formal syntax methods for natural language

This paper describes on-going research into the applications of some techniques normally used to formally specify and analyze the context-sensitive syntax of programming languages to the specification and analysis of the syntax of a natural language, namely English. The specific formal methods presently being investigated are two-level grammar (2LG) and the Vienna Definition Language (VDL). A preliminary subset of English has been established consisting of fifteen basic sentence patterns. 2LG and VDL specifications are given for one of these sentence types and the syntactic analysis of an English sentence using each of the two specifications is illustrated through an example.     

The search for robustness in natural language understanding

Practical natural language understanding systems used to be concerned with very small miniature domains only: They knew exactly what potential text might be about, and what kind of sentence structures to expect. This optimistic assumption is no longer feasible if NLU is to scale up to deal with text that naturally occurs in the "real world". The key issue is robustness: The system needs to be prepared for cases where the input data does not correspond to the expectations encoded in the grammar. In this paper, we survey the approaches towards the robustness problem that have been developed throughout the last decade. We inspect techniques to overcome both syntactically and semantically ill-formed input in sentence parsing and then look briefly into more recent ideas concerning the extraction of information from texts, and the related question of the role that linguistic research plays in this game. Finally, the robust sentence parsing schemes are classified on a more abstract level of analysis.Dept. of Computer Science, University of TorontoFor helpful comments on earlier drafts of this paper, I thank Judy Dick, Graeme Hirst, Diane Horton, Kem Luther, and Jan Wiebe. Financial support by the University of Toronto is acknowledged. Communication and requests for reprints should be directed to the author at Department of Computer Science, University of Toronto, Toronto, Canada M5S 1A4.     

Enhancing information systems management with natural language processing techniques

Natural language and databases are core components of information systems. They are related to each other because they share the same purpose: the conceptualization aspects of the real world in order to deal with them in some way. Natural language processing (NLP) techniques may substantially enhance most phases of the information system lifecycle, starting with requirements analysis, specification and validation, and going up to conflict resolution, result processing and presentation. Furthermore, natural language based query languages and user interfaces facilitate the access to information for anyone and allow for new paradigms in the usage of computerized services. This paper investigates the use of NLP techniques in the design phase of information systems. Then, it reports on data base querying and information retrieval enhanced with NLP.     

Natural language interfaces as expert systems r+or tv nd u strial applicatiqks

A human-machine natural language interface can be useful in converting natural language commands into the functional/procedural instructions required for operating one or the other of the commercially available IE software packages, the same pray the qualified analysts and engineers would have performed it. This paper first gives a review of the state of the art in related research areas and concludes that there is a need for the impetus of this research. The paper also presents the natural language interface ProcessTalk and its architectural design with its distinct modules.ProcessTalk's design ensures its portability between different industries and different software packages.An example application is also given.     

数据库汉语查询接口WTCDIS系统的设计与实现

Natural Language Query Interfaces to Database give users a more natural method to access databases. This research has significantly theoretical and practical value.In this paper , we first summarize some principles of Chinese language used in database query. Then we mainly explain the design and implementation of a natural language interface to database we have developed. Finally , preliminary empirical results from tests of this system are presented.     

A linguistic ontology of space for natural language processing

We present a detailed semantics for linguistic spatial expressions supportive of computational processing that draws substantially on the principles and tools of ontological engineering and formal ontology. We cover language concerned with space, actions in space and spatial relationships and develop an ontological organization that relates such expressions to general classes of fixed semantic import. The result is given as an extension of a linguistic ontology, the Generalized Upper Model, an organization which has been used for over a decade in natural language processing applications. We describe the general nature and features of this ontology and show how we have extended it for working particularly with space. Treaitng the semantics of natural language expressions concerning space in this way offers a substantial simplification of the general problem of relating natural spatial language to its contextualized interpretation. Example specifications based on natural language examples are presented, as well as an evaluation of the ontology's coverage, consistency, predictive power, and applicability.     

基于领域的自然语言篇章理解的实现方法

介绍了一种应用于知识系统的基于领域的自然语言理解篇章理解的实现方法.该方法的核心是聚类和关联,本质上是关注概念和概念间的关联.以本体作为知识表示的方法,通过对基本概念的聚类,把底层离散的概念进行初步的集中,再通过分析底层概念间的相互关联,把整个篇章中的基本概念都联系起来,得到一个篇章层次上的本体模型.     

基于知识图谱与关键词注意机制的中文医疗问答匹配方法

针对当前中文医疗领域高质量问答数据缺乏的问题,提出基于知识图谱与关键词注意机制的中文医疗问答匹配方法.首先,引入医学知识图谱,得到知识增强的句子特征.然后,加入关键词注意力机制,强调问题和答案句子之间的相互影响.在2个公开的中文医疗问答数据集cMedQA与webMedQA上的实验表明,当样本数据量较小时,文中方法的优势明显.消融实验也验证每个新增模块对文中方法的性能均有一定程度的提升.     

融合量子干涉信息的双重特征文本表示模型

在信息检索领域,量子干涉理论已应用于文档相关性、次序效应等核心问题的研究中,旨在建模用户认知引起的类量子干涉现象.文中从语言理解的需求出发,利用量子理论的数学工具分析语义组合过程中存在的语义演化现象,提出融合量子干涉信息的双重特征文本表示模型(Quantum Interference Based Duet-Feature Text Representation Model, QDTM).模型以约化密度矩阵为语言表示的核心组件,有效建模维度级别的语义干涉信息.在此基础上,构建捕获全局特征信息与局部特征信息的模型结构,满足语言理解过程中不同粒度的语义特征需求.在文本分类数据集和问答数据集上的实验表明,QDTM的性能优于量子启发的语言模型和神经网络文本匹配模型.     

A review of methods for automatic understanding of natural language mathematical problems

This article addresses the problem of understanding mathematics described in natural language. Research in this area dates back to early 1960s. Several systems have so far been proposed to involve machines to solve mathematical problems of various domains like algebra, geometry, physics, mechanics, etc. This correspondence provides a state of the art technical review of these systems and approaches proposed by different research groups. A unified architecture that has been used in most of these approaches is identified and differences among the systems are highlighted. Significant achievements of each method are pointed out. Major strengths and weaknesses of the approaches are also discussed. Finally, present efforts and future trends in this research area are presented.     

一种受限的自然语言通信方法研究与实现

论述了一个多智能体并行设计环境中自然语言通信模块的设计和实现方法。通过建立一个受限词汇的综合信息库,记录词汇的各种语法、语义信息;采用扩充短语结构文法对句子结构进行分析,定义了句子生成模板,智能体借助该模板提供的接口函数生成自然语言句子。该设计方法建立在受限语言的基础上,突出了在协商式多智能体之间进行自然语言通信时的准确性。此外,为智能体提供了灵活、友善的接口,提高了多智能体并行设计环境的性能。     

A Semantic Network of English: The Mother of All WordNets

We give a brief outline of the design and contents of the English lexical database WordNet, which serves as a model for similarly conceived wordnets in several European languages. WordNet is a semantic network, in which the meanings of nouns, verbs, adjectives, and adverbs are represented in terms of their links to other (groups of) words via conceptual-semantic and lexical relations. Each part of speech is treated differently reflecting different semantic properties. We briefly discuss polysemy in WordNet, and focus on the case of meaning extensions in the verb lexicon. Finally, we outline the potential uses of WordNet not only for applications in natural language processing, but also for research in stylistic analyses in conjunction with a semantic concordance.     

基于BIM与知识图谱的智能化审图系统设计与实现

针对传统建筑审图存在的人工依赖性强、数据难融合共享和审查内容可视化水平低等问题,提出一种基于BIM与知识图谱的建筑智能审图方法;首先,利用知识图谱技术将繁杂的建筑规范文本数据关联为结构化知识,建立知识与数据间的语义联系;其次,鉴于BIM模型的多源异构特性,提出基于IFC的建筑数据信息提取整合,为后续建筑智能化审图实现提供数据基础;最后基于HTML5网络框架和人工智能技术设计开发了具有三维可视化、跨平台等功能的建筑智能审图系统,实现了建筑模型网页端显示及可交互操作、审图内容可视化展示、规范条文对应显示等功能;经实际建筑项目实验测试,该系统有效实现了被审建筑模型的智能化审图,并自动检测出不符合规范的建筑模型构件,一定程度上提升建筑行业智能化审图水平。     

文法理论评述

介绍了在自然语言处理中常见的几种文法,并对它们的特点进行了简要的评述。