Feature Selection on Chinese Text Classification Using Character N-Grams
In this paper,we perform Chinese text classification using n-gram text representation on TanCorp which is a new large corpus special for Chinese text classification more than 14,000 texts divided into 12 clauses.We use different n-gram feature (1-,2-grams or 1-,2-,3-grams) to represent documents.Different feature weights (absolute text frequency,relative text frequency,absolute n-gram frequency and relative n-gram frequency) are compared.The sparseness of document by feature matrices is analyzed in various cases.We use the C-SVC classifier which is the SVM algorithm designed for the multi-classification task.We perform our experiments in the TANAGRA platform.We found out that the feature selection methods based on n-gram frequency (absolute or relative) always give better results and produce denser matrices.
Chinese text classification N-gram Feature selection
Zhihua Wei Duoqian Miao Jean-Hugues Chauchat Caiming Zhong
Tongji University,Key laboratory Embedded System and Service ComputingMinistry of Education,Shangh Tongji University,Key laboratory Embedded System and Service ComputingMinistry of Education,Shangh Université Lumière Lyon 2,Laboratoire ERIC,5 avenue Pierre Mendès-France,69676 Bron Cedex,France
国际会议
成都
英文
500-507
2008-05-17（万方平台首次上网日期，不代表论文的发表时间）