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汉语概率型上下文无关语法的自动推导   总被引:7,自引:2,他引:5  
周强  黄昌宁 《计算机学报》1998,21(5):385-392
本文提出了一种汉语概率型上下文无关语法的自动推导方法,它在匹配分析机制上实现了无指导的EM迭代训练算法,并通过对训练语料的自动短语界定预处理以及在集成不同知识源基础上构造合适始规则集  相似文献
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基于无秩树自动机的信息抽取技术研究   总被引:1,自引:0,他引:1  
针对目前基于网页结构的信息抽取方法的缺陷,提出了一种基于无秩树自动机的信息抽取技术,其核心思想是通过将结构化(半结构化)文档转换成无秩树,然后利用(k,l)-contextual树构造样本自动机,依据树自动机接收和拒绝状态来对网页进行数据的抽取.该方法充分利用结构,依托树自动机将传统的以单一结构途径的信息抽取方法与文法推理原则相结合,得到信息抽取规则.实验结果表明,该方法与同类抽取方法相比在准确率、召回率以及抽取所需时间上均有所提高.  相似文献
3.
面向Deep Web数据自动抽取的模板生成方法*   总被引:1,自引:0,他引:1       下载免费PDF全文
Deep Web结果页面大多由网站根据请求从后台数据库读取数据并动态填充到通用模板而生成的。研究如何从一系列同模板生成的页面中生成该模板,并利用模板自动抽取数据。给出了模板生成问题的形式化描述,提出了一种新颖的模板生成方法,利用生成的模板从实例网页中抽取数据。与现有方法相比,该方法适用于列表页面和详细页面两种类型网页。通过在多个领域站点上实验,说明新方法在不降低准确率的情况下能大大提高召回率。  相似文献
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In the area of programming languages, context-free grammars (CFGs) are of special importance since almost all programming languages employ CFG's in their design. Recent approaches to CFG induction are not able to infer context-free grammars for general-purpose programming languages. In this paper it is shown that syntax of a small domain-specific language can be inferred from positive and negative programs provided by domain experts. In our work we are using the genetic programming approach in grammatical inference. Grammar-specific heuristic operators and nonrandom construction of the initial population are proposed to achieve this task. Suitability of the approach is shown by examples where underlying context-free grammars are successfully inferred.  相似文献
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软件系统是由许多组件组合而成,当软件系统逐渐扩大以一定程度,可能超过人的理解能力,这时需要有一种能够自动归纳软件组件组织和结构的方法,软件体系结构的语法推断提供了这一种手段,本文在软件体系结构模糊语法描述的基础上,提出一种按模糊聚类进行语法推断的算法。  相似文献
6.
杨晓峰  孙明明  胡雪蕾 《计算机工程》2010,36(13):149-150,153
提出一种基于确定有限自动状态机(DFA)语法的网络攻击检测方法。正常的网络行为符合一定的语法规则,异常的行为会偏离正常的语法规则。通过对正常行为样本的学习得到基于DFA的语法,用学习得到的DFA模型检测针对网络服务器的应用层攻击。基于现实数据的对比实验表明该方法检测性能较好。  相似文献
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Building parsers is an essential task for the development of many tools, from software maintenance tools to any kind of business-specific, programmable environment having a command-line interface. Whilst grammars for many programming languages are available, these are, very often, almost useless because of the large diffusion of dialects and variants not contemplated by standard grammars. Writing a grammar by hand is clearly feasible, however it can be a tedious and error-prone task, requiring appropriate skills not always available. Grammar inference is a possible, challenging approach for obtaining suitable grammars from program examples. However, inference from scratch poses serious scalability issues and tends to produce correct, but meaningless grammars, hard to be understood and used to build tools. This paper describes an approach, based on genetic algorithms, for evolving existing grammars towards target (dialect) grammars, inferring changes from examples written using the dialect. Results obtained experimenting the inference of C dialect rules show that the algorithm is able to successfully evolve the grammar. Inspections indicated that the changes automatically made to the grammar during its evolution preserved its meaningfulness, and were comparable to what a developer could have done by hand.  相似文献
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