文献类型：期刊文献

英文题名：Automatic text categorization based on content analysis with cognitive situation models

作者：Guo, Yi[1,2];Shao, Zhiqing[1];Hua, Nan[3]

机构：[1]E China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Key Lab Comp Software Evaluat & Testing, Shanghai 201112, Peoples R China;[3]AF Engn Univ, Telecommun Engn Inst, Xian 710077, Peoples R China

年份：2010

卷号：180

期号：5

起止页码：613

外文期刊名：INFORMATION SCIENCES

收录：;EI(收录号：20095112563780);WOS:【SSCI(收录号:WOS:000273920600004)，SCI-EXPANDED(收录号:WOS:000273920600004)】；

基金：This work was jointly supported by Scientific Research Foundation for Returned Chinese Scholars (Ministry of Education of China) and Shanghai Municipal Natural Science Foundation (Grant No.09ZR1408400).We sincerely thank the anonymous reviewers and editors for their valuable comments that help to improve the quality of this paper significantly.

语种：英文

外文关键词：Text categorization; Content analysis; Cognitive situation models; Lexical/semantical analysis

摘要：Text categorization is an important research area of text mining. The original purpose of text categorization is to recognize, understand and organize different types of texts or documents. The general categorization approaches are treated as supervised learning, which infers similarity among a collection of categorized texts for training purposes. The existing categorization approaches are obviously not content-oriented and constrained at single word level. This paper introduces an innovative content-oriented text categorization approach named as CogCate. Inspired by cognitive situation models, CogCate exploits a human cognitive procedure in categorizing texts. In addition to traditional statistical analysis at word level, CogCate also applies lexical/semantical analysis. which ensures the accuracy of categorization. The evaluation experiments have testified the performance of CogCate. Meanwhile, CogCate remarkably reduces the time and effort spent on software training and maintenance of text collections. Our research work attests that interdisciplinary research efforts benefit text categorization. (C) 2009 Elsevier Inc. All rights reserved.