Computational Learning of Syntax
Computational Learning of Syntax
复制标题
语法的计算学习
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Alexander Clark
中科院分区：
文献类型：
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作者：
Alexander Clark
易学性传统上被认为是理论句法的一个重要限制；然而，所涉及的问题一直没有得到很好的理解，部分原因是缺乏针对各种形式语法的简单学习算法。在这里，我将讨论仅从符号字符串中学习分层结构语法所涉及的计算问题。所涉及的方法是基于一个语法范畴的派生上下文的抽象概念，这在最基本的无上下文语法情况下导致基于传统分布分析形式的学习算法。至关重要的是，这些技术可以扩展到使用轻度上下文敏感的语法（以及其他），从而产生原则上可以学习强大到足以表示所有自然语言的语法类的学习方法。这些学习方法要求语法的句法类别在某种技术意义上是可见的：它们必须具有良好的特征……
Learnability has traditionally been considered to be a crucial constraint on theoretical syntax; however, the issues involved have been poorly understood, partly as a result of the lack of simple learning algorithms for various types of formal grammars. Here I discuss the computational issues involved in learning hierarchically structured grammars from strings of symbols alone. The methods involved are based on an abstract notion of the derivational context of a syntactic category, which in the most elementary case of context-free grammars leads to learning algorithms based on a form of traditional distributional analysis. Crucially, these techniques can be extended to work with mildly context-sensitive grammars (and beyond), thus leading to learning methods that can in principle learn classes of grammars that are powerful enough to represent all natural languages. These learning methods require that the syntactic categories of the grammars be visible in a certain technical sense: They must be well charac...
表征 Petri 网的稳定性并推导有效的不等式
DOI：
10.3233/fi-2016-1374
发表时间：
2016
期刊：
Fundam. Informaticae
影响因子：
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作者：
Marvin Triebel;Jan Sürmeli
通讯作者：
Jan Sürmeli