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Text Matching and Categorization: Mining Implicit Semantic Knowledge from Tree-Shape Structures

机译：文本匹配和分类：从树形结构中挖掘隐式语义知识

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摘要

The diversities of large-scale semistructured data make the extraction of implicit semantic information have enormous difficulties. This paper proposes an automatic and unsupervised method of text categorization, in which tree-shape structures are used to represent semantic knowledge and to explore implicit information by mining hidden structures without cumbersome lexical analysis. Mining implicit frequent structures in trees can discover both direct and indirect semantic relations, which largely enhances the accuracy of matching and classifying texts. The experimental results show that the proposed algorithm remarkably reduces the time and effort spent in training and classifying, which outperforms established competitors in correctness and effectiveness.
机译：大规模半结构化数据的多样性使得隐式语义信息的提取具有巨大的困难。本文提出了一种自动无监督的文本分类方法，该方法使用树形结构表示语义知识，并通过挖掘隐藏结构而无需进行繁琐的词法分析来探索隐式信息。在树中挖掘隐含的频繁结构可以发现直接和间接的语义关系，从而大大提高了文本匹配和分类的准确性。实验结果表明，该算法显着减少了训练和分类所花费的时间和精力，其正确性和有效性均优于老对手。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第18期|723469.1-723469.9|共9页
  • 作者

    Guo Lin; Zuo Wanli; Peng Tao; Yue Lin;

  • 作者单位

    Jilin Univ, Minist Educ, Changchun 130000, Jilin, Peoples R China.;

    Jilin Univ, Minist Educ, Changchun 130000, Jilin, Peoples R China.;

    Jilin Univ, Minist Educ, Changchun 130000, Jilin, Peoples R China.;

    Jilin Univ, Minist Educ, Changchun 130000, Jilin, Peoples R China.;

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