采用预测策略的Earley算法

在自然语言处理中,句法分析主要有基于统计的方法和基于规则的方法。Earley算法是一种基于规则的方法,可以分析任意上下文无关文法(CFG),而不需要对文法进行修改。详细分析了Earley算法的特点。在通常的Earley算法中增加了多种预测机制,这些预测机制借鉴了LL,LR以及SLR等确定性分析算法的一些思想,并对这几种不同的预测机制及其组合在相同条件下进行了中文句法分析实验。结果显示,引入这些预测机制通常可以减少产生项目的数量,从而节省存储空间,减少运行时间。     

一种有效的结点标号上下文无关图文法分析算法

本文讨论了上下文无关图文法的性质，并证明了图文法推导具有独立性．本文还给出了一种有效的上下文无关图文法分析算法，它具有多项式时间复杂性，并给出了算法的正确性证明．该算法已经用Ｃ语言实现．     

含无序产生式的故事分析文法的研究

基于故事分析文法的故事理解法是本文作者在其博士论文中提出的一种面向故事深层含义的对语法、语义和语用综合进行理解的形式化方法，故事分析文法是一种高维的上下文有关文法，为了提高它的描述能力和表达范围，本文定义了一种特殊的产生式：无序产生式，并将它引入故事分析文法，讨论了由此带来的对故事分析文法的影响和无序产生式在故事分析文法中的性质，最后给出了含无序产生式的故事分析文法的一个子类文法的语法分析算法．     

基于Earley剖析的多功能雷达文法参数估计算法

针对基于随机上下文无关文法(Stochastic Context-Free Grammar,SCFG)建模的多功能雷达(Multi-Function Radars,MFR)参数估计问题的研究,在原有Inside-Outside(IO)算法和Viterbi-Score(VS)算法的基础上,提出一种基于Earley剖析的多功能雷达文法参数估计算法——EIO算法。该算法将IO算法与Earley剖析相结合,通过对截获的雷达数据进行预处理,可以处理任意形式的文法产生式,对文法产生式概率进行学习,从而实现MFR文法参数估计。通过理论分析和实验仿真,EIO算法可以在减少计算复杂度、记忆复杂度和运行时间的同时,有效保持文法参数估计精度,论证了方法的正确性和有效性。     

基于PRAM模型的CFGs并行识别与语法分析的扩充算法

本文介绍了一种PRAM模型上的上下文无关文法的并行识别和改进的并行语法分析方法金字塔结构,并对该方法进行了修改和补充,使其对非Chomsky规范形式,即文法的产生式右部候选式（即规则）有两个以上的非终结符连接的,或者候选式中既有非终结符,又有终结符的情况,扩充的算法也能识别和分析。     

一个上下文无关文法获取过程的设计和实现*

文章介绍一个基于复用的上下文无关文法获取过程的设计和实现,该过程用于获取以上下文无关文法表示的概念.它从待获取概念的有限实例和句型以及可能复用的已知概念出发,通过一个交互式文法推断过程,最终得到概念的文法定义.     

使用角色反演句法分析器RNA二级结构的预测

RNA二级结构预测问题是生物信息学的一个研究重点。该文主要利用自然语言理解中旬法分析的方法来研究RNA二级结构预测。使用基于角色反演算法建立起来的，采用概率上下文无关文法进行分析的句法分析器，来预测RNA二级结构。结合传统Chart算法分析器和广义LR算法分析器的优点，建立角色反演句法分析器；根据RNA二级结构的构建方法建立相应的概率上下文无关文法；给出对RNA二级结构进行预测的具体实例。     

基于上下文依赖规则覆盖的句子生成

基于规则覆盖的句子生成,是上下文无关文法句子生成的主要方法,但是它也具有局限性。最近提出的上下文依赖规则覆盖,能根据文法的内部结构不同而具有不同的分支集合,比规则覆盖的精度更高。目前,尚未见这种上下文依赖规则覆盖的句子生成算法。该文在规则覆盖的句子生成算法的基础上,实现一个基于上下文依赖规则覆盖的句子生成算法。该算法已在机器上实现并经过实验检验。     

量子上下文无关文法的代数性质

提出了量子上下文无关文法（l-VCFG）的概念,并研究了其具有的代数性质;证明了量子上下文无关文法（l-VCFG）和Chomsky范式文法（l-VCNF）以及Greibach范式文法（l-VGNF）的相互等价性;详细研究了量子上下文无关语言的代数刻画以及对于正则运算的封闭性。     

基于规则的模糊语句分析

针对当前常用的处理模糊语句的方法进行了分析,介绍了概率和规则相结合的消除模糊的方法:概率上下文无关文法,比较了几种方法的优缺点。     

一种自反馈汉语切词系统的研究和实现

探讨了一种自然语言理解(NLU)切词系统的设计思路。首先,综合运用各种传统分词方法,提出所有可能的切分结果,同时建立切词领域本体知识库;然后,结合切词领域本体知识库并运用概率统计和聚类的思想对切词结果进行划分,进入对应领域内,在自然语言理解的过程中进行基于语义分析的歧义排除;最后将理解结果反馈回切词系统,从而实现切词系统的自反馈和自我完善。本切词系统的特点是使切词系统和其服务的自然语言理解系统随着其运行共同获得可拓性发展,逐步达到最优化。     

Context-free like restrictions on selective rewriting

Selective substitution grammars based on ‘context-free’ productions form a possible framework for the study of ‘grammatically oriented’ formal language theory. Such grammars (with no control governing the composition of derivation steps) are studied in this paper. In particular we study the effect of various conditions on selectors (which define the way that rewriting is performed); those conditions are aimed to formalize the notion of ‘using information about the context’ during the rewriting process. Each of them captures a particular feature of a rewriting according to a context-free grammar or an EOS system (essentially a context-free grammar that can also rewrite terminal symbols). Some of those conditions yield characterizations of the class of context-free languages for other conditions the lower and upper bound on the language generating power are given. Also a natural notion of a class of ‘simple’ rewriting systems is introduced (pattern grammars) and it is demonstrated that they possess surprisingly high language generating power.     

An LR parsing technique for extended context-free grammars

Extended context-free grammars allow regular expressions to appear in productions right hand sides, and are a clear and natural way to describe the syntax of programming languages.In this paper an LR parsing technique for extended context-free grammars is presented, which is based on an underlying transformation of the grammar into an equivalent context-free one.The technique is suitable for inclusion in one-pass compilers: the implementation requires little extensions to the algorithms working for normal LR grammars. Besides describing the parsing method, the paper shows also the algorithms for deriving the parsing tables; tables optimization is also discussed. Finally, this technique is compared with other proposals appeared in the literature.     

A Toolkit for Generating Sentences from Context-Free Grammars

Producing sentences from a grammar, according to various criteria, is required in many applications. It is also a basic building block for grammar engineering. This paper presents a toolkit for context-free grammars, which mainly consists of several algorithms for sentence generation or enumeration and for coverage analysis for context-free grammars. The toolkit deals with general context-free grammars. Besides providing implementations of algorithms, the toolkit also provides a simple graphical user interface, through which the user can use the toolkit directly. The toolkit is implemented in Java and is available at http://lcs.ios.ac.cn/~zhiwu/toolkit.php. In the paper, the overview of the toolkit and the major algorithms implemented in the toolkit are presented, and experimental results and preliminary applications of the toolkit are also contained.     

Probabilistic context-free grammars estimated from infinite distributions

In this paper, we consider probabilistic context-free grammars, a class of generative devices that has been successfully exploited in several applications of syntactic pattern matching, especially in statistical natural language parsing. We investigate the problem of training probabilistic context-free grammars on the basis of distributions defined over an infinite set of trees or an infinite set of sentences by minimizing the cross-entropy. This problem has applications in cases of context-free approximation of distributions generated by more expressive statistical models. We show several interesting theoretical properties of probabilistic context-free grammars that are estimated in this way, including the previously unknown equivalence between the grammar cross-entropy with the input distribution and the so-called derivational entropy of the grammar itself. We discuss important consequences of these results involving the standard application of the maximum-likelihood estimator on finite tree and sentence samples, as well as other finite-state models such as hidden Markov models and probabilistic finite automata.     

A parsing methodology for the implementation of visual systems

The Visual Language Compiler-Compiler (VLCC) is a grammar-based graphical system for the automatic generation of visual programming environments. In this paper the theoretical and algorithmic issues of VLCC are discussed in detail. The parsing methodology we present is based on the “positional grammar” model. Positional grammars naturally extend context-free grammars by considering new relations in addition to string concatenation. Thanks to this, most of the results from LR parsing can be extended to the positional grammars inheriting the well known LR technique efficiency. In particular, we provide algorithms to implement a YACC-like tool embedded in the VLCC system for automatic compiler generation of visual languages described by positional grammars     

Formalized analysis of structural characteristics of large complexsystems

Macrostructural modelization is paramount to the development of large complex systems (LCS). The paper explores the macrostructural modelization of LCS in terms of a block diagram based model and a grammar based model. Firstly, the macrostructural modelization problem of LCS is formulated. Secondly, a block diagram based model is proposed and established for LCS. Specifically, two general-purpose information-processing modules are proposed and constructed, called perception cube and decision spheroid. Thirdly, a grammar based model is proposed and established for LCS through applying formal language theory to the block diagram based model. Specifically, perception cube and decision spheroid are visually represented as context-free grammars, named fusion grammar and synthesis grammar, respectively. Through a stratified constructive linkup between a stream of bottom-up growing fusion grammars and a stream of top-down growing synthesis grammars, a level of LCS is constructively defined and accordingly represented as a context-free grammar, named level grammar. Then, a whole LCS is represented as a context-free grammar through a compounding of all level grammars. Finally, a case study is presented to demonstrate the potential usability of the proposed and established models of LCS