A Concept Similarity Based Text Classification Algorithm

Jing Peng, Dongqing Yang, Shiwei Tang, Jun Gao, P. Zhang, Yan Fu
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引用次数: 10

Abstract

Text classification is an important task of data mining. Existing algorithms, which based on vector space models, does not considered concept similarities among words, so the accuracy of traditional text classification cannot guarantee. To solve the problem, this paper proposes a new text classification algorithm in Chinese text processing based on concept similarity. The contributions of the paper include: (1) proposing a new similarity-computing model between words or sentences based on concept similarity; (2) applying the algorithm successfully in the text classification of WEB news; (3). analyzing the similarity computing formulas systematically in theory; (4).proving that the algorithm has much more accurate than traditional k-NN algorithm in text classification problems through extensive experiments.
基于概念相似度的文本分类算法
文本分类是数据挖掘的一项重要任务。现有的基于向量空间模型的文本分类算法没有考虑词之间的概念相似度,无法保证传统文本分类的准确性。为了解决这一问题,本文提出了一种基于概念相似度的中文文本分类算法。本文的贡献包括:(1)提出了一种新的基于概念相似度的词或句子之间相似度计算模型;(2)将该算法成功应用于WEB新闻文本分类中;(3)从理论上系统分析了相似度计算公式;(4).通过大量的实验证明该算法在文本分类问题上比传统的k-NN算法具有更高的准确率。
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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