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Synonymy Extraction From Semantic Networks Using String and Graph Kernel Methods

机译:使用字符串和图形内核方法从语义网络中提取同义词

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Synonyms are a highly relevant information source for natural language processing. Automatic synonym extraction methods have in common that they are either applied on the surface representation of the text or on a syntactical structure derived from it. In this paper, however, we present a semantic synonym extraction approach that operates directly on semantic networks (SNs), which were derived from text by a deep syntactico-semantic analysis. Synonymy hypotheses are extracted from the SNs by graph matching. These hypotheses are then validated by a support vector machine (SVM) employing a combined graph and string kernel. Our method was compared to several other approaches and the evaluation has shown that our results are considerably superior.
机译:同义词是自然语言处理的高度相关信息源。自动同义词提取方法共同,它们是应用于文本的表面表示或衍生自IT的语法结构。然而,在本文中,我们提出了一种直接在语义网络(SNS)上运行的语义同义词提取方法,该方法是通过深度句法语义分析从文本派生的。同义假设通过图形匹配从SNS中提取。然后由采用组合图和串内核的支持向量机(SVM)验证这些假设。我们的方法与其他几种方法进行了比较,评价表明我们的结果大大优越。

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