逐步求精法获取上下文无关文法

文法推断研究如何从语言的有限实例,通过归纳推断获取语言的文法定义。文中提出一个基于逐步求精的上下文无关文法推断方法,以尝试将文法推断用于替代或帮助传统手工的文法构造工作。文中的推断方法以Ａｎｇｌｕｉｎｈ的交互式学习模型为框架,以逐步求精和复用为主要策略,具有增量式获取结构自然的文法的特点。     

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.     

上下文无关文法推断中的几条启发规则及其应用

本文先简要介绍了一种上下文无关文法的推断方法--逐步求精法，然后论述了递归概念在文法推断中的核心作用，并从递归概念的特殊性质出发提出了多条启发规则，能有效减少无效探求和与用户交互的次数，尤其适合于文法较复杂、例句集信息量较大的情况。这些启发规则同时也适用于对上下文无关文法的其它推断方法。     

范畴文法接受语言的能力

本文研究Ades和steedman提出的范畴文法.与上下文无关文法相比较,我们讨论具有不同的约简规则集的范畴文法接受语言的能力.使用F规则和FP规则时,接受的语言恰好是上下文无关语言.使用口规则和FP规则时,接受的语言包含上下文无关语言为其真子集.使用F规则、FP规则和BP规则时,该文法不能接受某些上下文无关语言.     

推导可交换上下文无关语言

提出了推导可交换上下文无关语言及其文法,证明了正规语言类和有界上下文无关语言类都是推导可交换上下文无关语言类的子集,而推导可交换上下文无关语言类是上下文无关语言类的一个子集;定义了该类语言的α闭包等有关运算,给出了推导可交换上下文无关语言表达式,证明了推导可交换上下文无关文法、推导可交换上下文无关语言表达式之间的等价转换.     

可交换上下文无关文法

本文提出了可交换上下文无关文法及其该文法产生的语言——可交换上下文无关语言，证明了正规语言类是可交换上下文无关语言类的一个子集，而可交换上下文无关语言类是上下文无关语言类的一个子集；讨论了可交换上下文无关语言的结构特点，并给出了可交换上下文无关语言的Pumping引理。     

格值下推自动机与格值上下文无关文法

引入了格值下推自动机、格值上下文无关文法及它们的语言的概念,证明了格值下推自动机以两种不同方式接受的语言类的等价性,研究了格值Chomsky范式文法、格值上下文无关文法及其派生所产生的语言的等价条件,揭示了在一定条件下,格值下推自动机接受的语言类与格值上下文无关文法产生的语言类的等价性,证明了有理格值语言均被格值下推自动机识别。     

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。     

量化上下文无关语言的代数性质

通过引入量化下推自动机与量化上下文无关文法的定义,研究了以两种不同方式接受语言的量化下推自动机等价性问题,证明了在可交换的双幺赋值幺半群上,量化下推自动机接受的语言与量化上下文无关文法生成的语言相同。     

一种特殊的上下文无关文法及其语法分析

SAQ系统是一个进行软件规约获取、检验和复用的实验系统，其中以上下文无关文法表示的概念是规约的一部分.SAQ要求将概念的词法和句法定义结合在一个上下文无关文法中.如果用常规的上下文无关文法描述诸如程序设计语言和自然语言等一些复杂概念的语法，则需要把诸如空格和回车等没有实质意义的分隔符包含到语法中去(这种描述方法称为朴素表示法),使得语法描述很累赘.为此，作者设计了一种特殊的上下文无关文法,它把通常上下文无关文法定义中的非终极符集合和终极符集合进行细化.用这种文法可以相对简洁地描述程序语言和自然语言等复杂概     

Evolutionary induction of stochastic context free grammars

This paper describes an evolutionary approach to the problem of inferring stochastic context-free grammars from finite language samples. The approach employs a distributed, steady-state genetic algorithm, with a fitness function incorporating a prior over the space of possible grammars. Our choice of prior is designed to bias learning towards structurally simpler grammars. Solutions to the inference problem are evolved by optimizing the parameters of a covering grammar for a given language sample. Full details are given of our genetic algorithm (GA) and of our fitness function for grammars. We present the results of a number of experiments in learning grammars for a range of formal languages. Finally we compare the grammars induced using the GA-based approach with those found using the inside-outside algorithm. We find that our approach learns grammars that are both compact and fit the corpus data well.     

The Complexity of Universal Text-Learners

The present work deals with language learning from text. It considers universal learners for classes of languages in models of additional information and analyzes their complexity in terms of Turing degrees. The following is shown: If the additional information is given by a set containing at least one index for each language from the class to be learned but no index for any language outside the class, then there is a universal learner having the same Turing degree as the inclusion problem for recursively enumerable sets. This result is optimal in the sense that any other successful learner has the same or higher Turing degree. If the additional information is given by the index set of the class of languages to be learned then there is a computable universal learner. Furthermore, if the additional information is presented as an upper bound on the size of some grammar that generates the language, then a high oracle is necessary and sufficient. Finally, it is shown that for the concepts of finite learning and learning from good examples, the index set of the class to be learned gives insufficient information due to the restrictive convergence constraints, these criteria need the jump of the index set instead of the index set itself. So, they have infinite access to the information of the index set in finite time.     

A memetic grammar inference algorithm for language learning

An unsupervised incremental algorithm for grammar inference and its application to domain-specific language development are described. Grammatical inference is the process of learning a grammar from the set of positive and optionally negative sentences. Learning general context-free grammars is still considered a hard problem in machine learning and is not completely solved yet. The main contribution of the paper is a newly developed memetic algorithm, which is a population-based evolutionary algorithm enhanced with local search and a generalization process. The learning process is incremental since a new grammar is obtained from the current grammar and false negative samples, which are not parsed by the current grammar. Despite being incremental, the learning process is not sensitive to the order of samples. All important parts of this algorithm are explained and discussed. Finally, a case study of a domain specific language for rendering graphical objects is used to show the applicability of this approach.     

A bibliographical study of grammatical inference

The field of grammatical inference (also known as grammar induction) is transversal to a number of research areas including machine learning, formal language theory, syntactic and structural pattern recognition, computational linguistics, computational biology and speech recognition. There is no uniform literature on the subject and one can find many papers with original definitions or points of view. This makes research in this subject very hard, mainly for a beginner or someone who does not wish to become a specialist but just to find the most suitable ideas for his own research activity. The goal of this paper is to introduce a certain number of papers related with grammatical inference. Some of these papers are essential and should constitute a common background to research in the area, whereas others are specialized on particular problems or techniques, but can be of great help on specific tasks.     

Generating valid grammar-based test inputs by means of genetic programming and annotated grammars

Automated generation of system level tests for grammar based systems requires the generation of complex and highly structured inputs, which must typically satisfy some formal grammar. In our previous work, we showed that genetic programming combined with probabilities learned from corpora gives significantly better results over the baseline (random) strategy. In this work, we extend our previous work by introducing grammar annotations as an alternative to learned probabilities, to be used when finding and preparing the corpus required for learning is not affordable. Experimental results carried out on six grammar based systems of varying levels of complexity show that grammar annotations produce a higher number of valid sentences and achieve similar levels of coverage and fault detection as learned probabilities.     

概率图模型研究进展综述

概率图模型作为一类有力的工具,能够简洁地表示复杂的概率分布,有效地(近似)计算边缘分布和条件分布,方便地学习概率模型中的参数和超参数.因此,它作为一种处理不确定性的形式化方法,被广泛应用于需要进行自动的概率推理的场合,例如计算机视觉、自然语言处理.回顾了有关概率图模型的表示、推理和学习的基本概念和主要结果,并详细介绍了这些方法在两种重要的概率模型中的应用.还回顾了在加速经典近似推理算法方面的新进展.最后讨论了相关方向的研究前景.     

An Interactive Learning Algorithm for Acquisition of Concepts Represented as CFL

In this paper,an interactive learning algorithm of context-frmm language is presented.This algorithm is designed especially for system SAQ,which is a system for formal secification acquisition and verification.As the kernel of concept acquisition subsystem(SAQ/CL)of SAQ,the algorithm has been implemented on SUN SPARC workstation.The grammar to be obtained can represent sentence structure naturally.     

Learning tree languages from positive examples and membership queries

We investigate regular tree languages’ exact learning from positive examples and membership queries. Input data are trees of the language to infer. The learner computes new trees from the inputs and asks the oracle whether or not they belong to the language. From the answers, the learner may ask further membership queries until he finds the correct grammar that generates the target language. This paradigm was introduced by Angluin in the seminal work [D. Angluin, A note on the number of queries needed to identify regular languages, Information and Control 51 (1981) 76–87] for the case of regular word languages. Neither negative examples, equivalence queries nor counter-examples are allowed in this paradigm.     

形式语言与自动机中关于ε的一些问题

讨论了形式语言与自动机理论中关于空串ε的一些问题。分析了ε产生式对文法和语言分类的影响;从文法和有限状态自动机的角度讨论了开始符号S和开始状态q0的作用;提出了语言增加或减少ε句子的简单方法;研究了ε-NFA的ε状态转换函数的本质;提出了ε-NFA转换为NFA的新方法,即先将ε-NFA转换为文法形式,消除ε产生式和单产生式后得到正则文法,再将正则文法转换为NFA。并用实际例子进行了验证。