Improved Terms Weighting Algorithm of Text

Ma Zhanguo, Feng Jing, Hu Xiangyi, Shi Yanqin, Chen Liang
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引用次数: 6

Abstract

Most of traditional information retrieval and automatic text classification methods with vector space model almost need determine the weighting of the feature terms. Term weighting plays an important role to achieve high performance in information retrieval and text classification. The popular method is using term frequency (tf) and inverse document frequency (idf) for representing importance and computing weighting of terms. But the tf-idf model is not introduced class information, the important information such as title, abstract, conclusion, and the synonymous words information. This paper provides an improved method to compute weighting of the terms. The above information is involved. The experimental results show that the performance is enhanced. The role of important and representative terms is raised and the effect of the unimportant feature term to retrieval and classification is decreased. In addition, the F1 based on new algorithm is higher than based on traditional tf-idf model.
改进的文本术语加权算法
传统的基于向量空间模型的信息检索和文本自动分类方法大多需要确定特征项的权重。术语加权在信息检索和文本分类中起着重要的作用。常用的方法是使用词频率(tf)和逆文档频率(idf)来表示词的重要性和计算词的权重。但是tf-idf模型没有引入类信息、标题、摘要、结论等重要信息和同义词信息。本文提出了一种计算项权重的改进方法。涉及以上信息。实验结果表明,该方法提高了系统的性能。提高了重要和代表性词的作用,降低了不重要特征词对检索和分类的影响。此外,基于新算法的F1比基于传统tf-idf模型的F1要高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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