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Incremental Learning of Context Free Grammars by Bridging Rule Generation and Search for Semi-optimum Rule Sets

机译:通过桥接规则生成和搜索半最佳规则集的上下文无关文法的增量学习

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This paper describes novel methods of learning general context free grammars from sample strings, which are implemented in Synapse system. Main features of the system are incremental learning, rule generation based on bottom-up parsing of positive samples, and seaxch for rule sets. From the results of parsing, a rule generation process, called "bridging," synthesizes the production rules that make up any lacking parts of an incomplete derivation tree for each positive string. To solve the fundamental problem of complexity for learning CFG, we employ methods of searching for non-minimum, semi-optimum sets of rules as well as incremental learning based on related grammars. One of the methods is search strategy called "serial search," which finds additional rules for each positive sample and not to find the minimum rule set for all positive samples as in global search. The other methods are not to minimize nonterminal symbols in rule generation and to restrict the form of generated rules. The paper shows experimental results and compares various synthesis methods.
机译:本文介绍了从样本字符串中学习通用上下文无关文法的新方法,这些方法在Synapse系统中实现。该系统的主要功能是增量学习,基于正样本的自底向上解析的规则生成以及规则集的seaxch。根据解析的结果,称为“桥接”的规则生成过程将为每个正字符串合成构成不完整派生树中任何缺少部分的生产规则。为了解决学习CFG的复杂性这一基本问题,我们采用了搜索非最小,半最佳规则集以及基于相关语法的增量学习的方法。其中一种方法是称为“序列搜索”的搜索策略,它可以为每个阳性样本找到其他规则,而不是像在全局搜索中那样为所有阳性样本找到最小规则集。其他方法不是在规则生成中最小化非终结符,也不是限制生成规则的形式。本文显示了实验结果并比较了各种合成方法。

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