基于同义词合并的文本分类特征选择与加权研究

Zhenyu Lu, Yongmin Liu, Shuang Zhao, Xuebin Chen
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引用次数: 8

摘要

特征选择和加权是文本分类的关键问题之一。特征选择的主要障碍是噪声和稀疏性。提出了一种基于语义统计的中文文本特征选择和加权方法。首先,基于同义词库,利用同义概念提取文本中的特征值。然后,我们引入了一个新的基于词频和熵的权函数,该权函数根据词频的强度来调整词频在分类器中的作用。实验表明,该方法优于传统的特征选择方法,提高了文本分类系统的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on Feature Selection and Weighting Based on Synonym Merge in Text Categorization
Feature selection and weighting is one of the key problem in text categorization. The chief obstacles to feature selection are noise and sparseness. This paper presents an approach of Chinese text feature selection and weighting based on semantic statistics. First, we use synonymous concepts to extract feature values in text based on Thesaurus which names TongYiCi CiLin. Then, we introduce a new weight function based on term frequency and entropy, which adjusts the effect of the feature term in the classifier according to the feature term’s strength. Experiments show that our method is much better than kinds of traditional feature selection methods and it improve the performance of text categorization systems.
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