该文提出一种词类频率和关联中文文本分类相结合的算法ARCTC。此算法将文档视作事务,关键词视作项,并针对文本事务的特性,提出利用词的类频率筛选与分类相关性不大的词汇,然后将改进的关联规则挖掘算法用于挖掘项和类别间的相关关系。挖掘出的规则用于形成类别特征词的集合,可用来和类标号未知文档的词的集合求交集,交集元素个数最多者即为所分类别。实验证明,该算法在提高训练时间和测试时间的同时具有较好的召回率、准确率和F-Measure。
Abstract
In this paper , a new algorithm that integrates class frequency into association rules based document classification is introduced into Chinese text categorization. This algorithm views each document as a transaction and each term as an item. The class frequency of a term is used to filter the words that are irrelevant to classification , and the mining algorithm of association rules is used to mine the correlation between item and category. Class character words sets are formed basing on the rules , and unlabeled documents are classified by intersecting with these sets. Experiments confirm that this method has a promising recall , precision rate and F-Measure while speeding up both training and test time.
关键词
计算机应用 /
中文信息处理 /
基于关联的分类 /
中文文本分类 /
词类频率 /
类别特征词集合
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Key words
computer application /
Chinese information processing /
association based classification /
chinese text categorization /
term class frequency /
class character term set
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参考文献
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脚注
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基金
科技部科技电子政务系统关键技术及应用系统的研究资助(2001BA110B01)
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