属性文法中变元间的关系

本文从模式的语义、句法描述着眼,分析了一个属性文法的产生式变元间的几种关系,把 这些关系作为语义部分的内容,从而推广了属性文法,以推广后的属性文法为基础,引入控制 连接图作为主要的限制条件,提出一种"语义程序文法",并把标准模式与畸变模式之间的联系 用限制条件描述.最后利用变元间的关系,把模式的描述与知识的表达联系起来.     

模式识别的一类属性文法

本文叙述了在通常的文法中引入两个基元间连接属性的重要性,从而看出在属性文法 中,句法和词意两部分间可以有折衷的关系,即增加后者的复杂性将会使前者得到简化,反之 亦然,这样使便于应用的有限状态属性文法成为基本的形式.这里作者吸取了PDL,Plex Grammar,Tree Grammar中连接关系的优点,引入连接属性,提出一类关联属性文法.这类 文法可以有效地描述和识别曲线段构成的图形,如汉字、电子线路图等.     

对火车号句法模式识别的探讨

句法模式识别是模式识别中普遍使用的一种先进技术，若要找到一种合适的结构文法来组织句子描述图形，先要对图形进行预处理，特别是线图形化处理，本文提出了“七段码”投影线图形化的新思想，讨论了对火车号句法模式识别的技术。     

属性文法的研究与应用

本文详细讨论了有关属性文法的基本概念,属性文法的两种分类方法,两种分类之间的关系及属性文法的判定问题。本文还讨论了如何用属性文法定义编译程序的遍体制说明及与指称语义的关系。最后讨论了属性文法在编译程序中的应用与属性计算器的程序处理。     

对火车号句法模式识别的探讨

句法模式识别是模式识别中普遍使用的一种先进技术，若要找到一种合适的结构文法来组织句子描述图形，先要对图形进行预处理，特别是线形化处理，本文提出了“七段码”投影线图形化的新思想，讨论了火车号句法模式识别的技术。     

一种适合于描述无限制手写字符图象的语言CPDL

人们曾为解决特定的识别问题而提出了许多图象描述语言。但到目前为止,还没有任何一种能有效地克服手写字符的形状差异对句法识别的影响。本文在K.S.Fu的形状属性文法的基础上,发展了一种带有位置和形状属性的文法。它只需7个简单的模式元,句子的长度可以人为地加以控制。这种语言的优点是较好地平衡了属性文法的句法部分和词意部分的相对关系,具有较强的描述能力及灵活性,能有效地克服形状的各种变化对句法识别的影响。     

模式识别的一种词意句法距离度量

本文讨论噪声模式和畸变模式的识别问题.用带有位置坐标的树状文法描述图象,对区 域的边界进行分析,建立与产生式相对应的词意规则.这里既考虑模式的统计特征,又通过词 意、句法指导下的变换来描写畸变模式的结构.在此基础上提出一种包括词意及句法的距离 度量,从而用最小距离准则来进行识别.     

识别在线手写汉字的模糊属性自动机

本文提出一种识别在线手写汉字笔划的模糊属性自动机,为汉字识别打下了基础.汉字 的笔划由笔段构成,利用笔段的长度信息,借助模糊信息处理方法,以不变嵌入原理为着眼点, 提出一种模糊属性文法及其相应的模糊属性自动机.这种文法在句法形式上是有限状态文法, 但语义规则中包含上下文的信息,其能力大大超过有限状态文法,相应的自动机能有效地识别 笔划.经对大量在线手写汉字的识别,表明了模糊属性自动机的实用性.     

模式识别中的递归结构及其句法-词义描述

模式的递归结构影响它的形式语言性质,成为模式结构描述的复杂问题之一.本文系统 地研究了递归结构的性质,按基本递归结构划分子模式,并以句法-词义方法对它们作分层文 法描述,从而建立了递归结构的有效描述体系.程序文法、属性文法以及由此派生的递归条件 文法和递归属性文法,都能成功地按此体系描述各种递归结构,这些文法的描述能力也因而得 到阐明.     

基于属性文法和语义网络的综合知识表示模型

本文提出了一个属性文法计算模型与语义网络表示模型相结合的综合知识表示模型.根据形式化的语义网络表示模型和属性文法的特点,该模型使用属性文法的符号建立了一些适合于语义网络表示模型的语法和语义规则模式,并且可通过扩充的属性文法的解释器来实现推理.     

A linguistic approach to 3-D object recognition

The principles of a 3-D object recognition system for combined intensity-image and depth-map understanding are discussed. The goal of such system is to be an inversion of image synthesis performed by 3-D computer graphics. A linguistic model for two system elements, the knowledge base and recognition strategy, being an extension of pattern recognition approaches, is outlined. It consists of a powerful object specification language and a simultaneous syntactic-semantic analysis in this language. The syntax is based on a node-controlled parallel structure grammar. Particular attention is paid to elements shared in common by several parts and to hidden line/surface problems. Both are embedded into the grammars derivation. The semantics is well-defined due to the attribution of the grammar.     

基于HowNet的句子褒贬倾向性研究

文本倾向性识别在信息过滤、自动文摘、文本分类等领域有广泛的应用前景。句子倾向性研究是文本倾向性识别的基础,结合句法分析结果和词语语义倾向性可以衡量句子褒贬倾向性。以HowNet的词汇语义相似度计算为基础,提出了基于的语义距离和语法距离的句子褒贬倾向性计算方法。大量语句实验表明,该方法的计算结果与人工判别结果更接近。     

RFuzzy: Syntax, semantics and implementation details of a simple and expressive fuzzy tool over Prolog

We present the RFuzzy framework, a Prolog-based tool for representing and reasoning with fuzzy information. The advantages of our framework in comparison to previous tools along this line of research are its easy, user-friendly syntax, and its expressivity through the availability of default values and types.In this approach we describe the formal syntax, the operational semantics and the declarative semantics of RFuzzy (based on a lattice). A least model semantics, a least fixpoint semantics and an operational semantics are introduced and their equivalence is proven. We provide a real implementation that is free and available. (It can be downloaded from http://babel.ls.fi.upm.es/software/rfuzzy/.) Besides implementation details, we also discuss some actual applications using RFuzzy.     

Operational Semantics for MSC'96

Recently, the ITU-standardised specification language Message Sequence Chart has been extended with constructs for more complete and structured specifications. The new version of the language is called MSC'96. Currently, research is performed on the extension of the formal semantics towards a semantics for MSC'96. In this article, we aim at explaining the basic ideas behind the formal semantics. We give formal definitions of parts of the language, but most features are explained by informal examples and drawings. It takes several steps in order to follow the path from an MSC drawing to its formal meaning. First, the drawing must be converted to a concrete textual representation. This conversion is already defined implicitly in Z.120. Next, this syntax is transformed into a formal expression over some process algebra signature. MSC constructs are replaced by appropriate process algebra operators. This transformation is compositional. The operational behaviour of the process algebraic expression can be studied, or the expression can be interpreted into some mathematical model and compared to the interpretation of some other MSC.     

A comparative study of two formal semantics of the SIGNAL language

SIGNAL is a part of the synchronous languages family, which are broadly used in the design of safety-critical real-time systems such as avionics, space systems, and nuclear power plants. There exist several semantics for SIGNAL, such as denotational semantics based on traces (called trace semantics), denotational semantics based on tags (called tagged model semantics), operational semantics presented by structural style through an inductive definition of the set of possible transitions, operational semantics defined by synchronous transition systems (STS), etc. However, there is little research about the equivalence between these semantics. In this work, we would like to prove the equivalence between the trace semantics and the tagged model semantics, to get a determined and precise semantics of the SIGNAL language. These two semantics have several different definitions respectively, we select appropriate ones and mechanize them in the Coq platform, the Coq expressions of the abstract syntax of SIGNAL and the two semantics domains, i.e., the trace model and the tagged model, are also given. The distance between these two semantics discourages a direct proof of equivalence. Instead, we transformthem to an intermediate model, which mixes the features of both the trace semantics and the tagged model semantics. Finally, we get a determined and precise semantics of SIGNAL.     

PAU: PARSING AND UNDERSTANDING WITH UNIFORM SYNTACTIC, SEMANTIC, AND IDIOMATIC REPRESENTATIONS

PAU is an all-paths chart-based unification parser that uses the same uniform representation for regular syntax, irregular syntax such as idioms, and semantics. PAU's representation has very little redundancy, simplifying the task of adding new semantics and syntax fo PAU's knowledge base. PAU uses relations between the syntax and semantics to avoid the proliferation of rules found in semantic grammars. By encoding semantics at the same level of representation as syntax, PAU is able to use semantic constraints early in the parse to eliminate semantically anomalous syntactic interpretations. Examples are given to show how PAU can handle the many eccentricities of different idioms using the same mechanisms as are used to handle regular syntax and semantics. These include the ability of some idioms, but not other idioms of the same syntactic form to undergo passivization, particle movement, action nominalization, indirect object movement, modification by adjectives, gerundive nominalization, prepositional phrase preposing, and topicalization. PAU's representation is bidirectional and is also used by a companion generator. PAU is designed to be efficient, runs in real time on typical workstations, and is being used in a number of natural language systems.     

Probabilistic Pattern Matching and the Evolution of Stochastic Regular Expressions

The use of genetic programming for probabilistic pattern matching is investigated. A stochastic regular expression language is used. The language features a statistically sound semantics, as well as a syntax that promotes efficient manipulation by genetic programming operators. An algorithm for efficient string recognition based on approaches in conventional regular language recognition is used. When attempting to recognize a particular test string, the recognition algorithm computes the probabilities of generating that string and all its prefixes with the given stochastic regular expression. To promote efficiency, intermediate computed probabilities that exceed a given cut-off value will pre-empt particular interpretation paths, and hence prune unconstructive interpretation. A few experiments in recognizing stochastic regular languages are discussed. Application of the technology in bioinformatics is in progress.     

Incorporating linguistic structure into maximum entropy language models

In statistical language models,how to integrate diverse linguistic knowledge in a general framework for long-distance dependencies is a challenging issue,In this paper,an improved language model incorporating linguistic structure into maximum entropy framework is presented.The poposed model combines trigram with the structure knowledge of base phrase in which trigram is used to capture the local relation between words.while the structure knowledge of base phrase is considered to represent the long-distance relations between syntactical structures.The knowledge of syntax,semantics and vocabulary is is integrated into the maximum entropy framework,Experimental results show that the proposed model improves by 24% for language model perplexity and increases about3% for sign language recognition rate compared with the trigram model.