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SVM-Based Semantic Text Categorization for Large Scale Web Information Organization

机译:基于SVM的大规模Web信息组织的语义文本分类

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Traditional web information service can't meet the demand of users getting personalized information timely and properly, which can be think as a kind of passive information organization method. In this paper, an adaptive and active information organization model in complex Internet environment is proposed to provide personalized information service and to automatically retrieve timely, relevant information. An SVM-based Semantic text categorization method is adopted to implement adaptive and active information retrieval. Performance experiment based on a prototype retrieval system manifests the proposed schema is efficient and effective.
机译:传统的Web信息服务无法满足用户及时获得个性化信息的需求,可以作为一种被动信息组织方法。在本文中,提出了复杂的互联网环境中的自适应和活动信息组织模型来提供个性化信息服务并自动检索相关信息。采用基于SVM的语义文本分类方法来实现自适应和活动信息检索。基于原型检索系统的性能实验表现出所提出的架构有效且有效。

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