利用交叉分类机制共享因特网上各种语言的信息资源是知识挖掘的重要方法,本文给出了双语交叉分类的模型以及实现方法。其主要思想是不需要进行机器翻译和人工标注,利用文本特征抽取机制提取类别特征项和文本特征项,通过基于概念扩充的对译映射规则自动生成类别和文本特征向量,在此基础上利用潜在语义分析,将双语文本在语义层面上统一起来,通过类别与文本的语义相似度进行分类。从而获取较高的精度。
Abstract
It is essential to knowledge discovery that multi-linguistic text categorization is applied to share the information sources in the Internet . The model for bi-linguistic text categorization is presented in this paper. It utilizes the mechanism of text feature extraction to extract the features of classes and texts ,and it generates the feature vectors of classes and texts by the rule of word translation based on concept expansion. As a result ,it uses Latent Semantic Indexing to integrate the bi-linguistic texts on the semantic layer ,and it calculates the semantic similarity between texts and classes to classify the texts. It can make high categorization precision ,and it is independent of machine translation and manual tagging.
关键词
双语交叉文本分类 /
概念扩充 /
潜在语义分析 /
空间向量模型
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Key words
bi-linguistic text categorization /
conceptual expansion /
latent semantic indexing /
vectorpace model
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参考文献
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