基于图的文本表示模型及其实现

Faguo Zhou, Fan Zhang, Bingru Yang
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引用次数: 22

摘要

本文在总结常用的几种文本表示模型,如布尔模型、概率模型、向量空间模型等的基础上,主要针对向量空间模型的缺陷,提出了词语义空间。在词语义空间中,设计了基于图的文本表示模型。给出了这类文本表示模型的一些性质,该模型可以描述文本中单词的语义约束。同时,该模型还可以解决向量空间模型的缺陷,如词的顺序、句子和短语之间的边界等。最后提出了文本相似度的计算方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graph-based text representation model and its realization
In this paper, on the foundation of summarizing several common used text representation models, such as Boolean model, probability model, vector space model and so on, mainly according to the defects of the vector space model, the word semantic space is proposed. And in the word semantic space, a graph-based text representation model is designed. Some properties of this type of text representation model have been given, and this model can describe the words semantic constraints in the text. At the same time, this model can also solve the defects of vector space model, such as the order or words, the boundary between sentences and phrases, etc. And at last the method of computing the text similarity is put forward.
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