Modeling Graph Languages with Grammars Extracted via Tree Decompositions
Modeling Graph Languages with Grammars Extracted via Tree Decompositions
复制标题
使用通过树分解提取的语法对图语言进行建模
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Mark Johnson
中科院分区：
文献类型：
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作者：
Bevan K. Jones;S. Goldwater;Mark Johnson
自然语言的概率模型的工作往往集中在字符串和树，但有越来越多的兴趣，更一般的图形形状的结构，因为它们似乎更适合表示自然语言的语义，本体，或其他种类的知识结构。然而，虽然有相对简单的方法来定义字符串和树上的生成模型，但对于更一般的图来说，它更具挑战性。本文描述了n-gram到图的自然推广，利用超边替换文法来定义图语言的生成模型。
Work on probabilistic models of natural language tends to focus on strings and trees, but there is increasing interest in more general graph-shaped structures since they seem to be better suited for representing natural language semantics, ontologies, or other varieties of knowledge structures. However, while there are relatively simple approaches to defining generative models over strings and trees, it has proven more challenging for more general graphs. This paper describes a natural generalization of the n-gram to graphs, making use of Hyperedge Replacement Grammars to define generative models of graph languages.