文本降维中表达特征的一种新代数

Xin Guo, Yang Xiang, Qian Chen
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引用次数: 0

摘要

文本挖掘的难点在于涉及自然语言的多语料库和高维。来自数据集的特征需要组合或铰接。本文旨在开发一种基于本体的文本特征对齐方法,为文本降维奠定基础。首先,定义了一个新的文本特征图,并在此基础上进行发音。其次,提出了文本特征图计算的代数系统。最后,通过实例验证了该方法的有效性和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel algebra to articulate feature in text dimension reduction
Challenges in text mining arise from multi-corpus and high dimensionality involving natural language. Features from datasets needs to be composed or articulated. This paper aims to develop a new approach to align text feature using ontology, which can form the base of text dimension reduction. Firstly, a novel text feature graph is defined, based on which we can do articulation. Secondly, an algebra system is proposed for text feature graph computing. Finally, an instance is demonstrated to show the efficiency and accuracy of the proposed approach.
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