document: Extracting key-substring-group features for text classification In many text classification applications , it is appealing to take every document as a string of characters rather than a bag of words . Previous research studies in this area mostly focused on different variants of generative Markov chain models . Although discriminative machine learning methods like Support Vector Machine ( SVM ) have been quite successful in text classification with word features , it is neither effective nor efficient to apply them straightforwardly taking all substrings in the corpus as features . In this paper , we propose to partition all substrings into statistical equivalence groups , and then pick those groups which are important ( in the statistical sense ) as features ( named key-substring-group features ) for text classification . In particular , we propose a suffix tree based algorithm that can extract such features in linear time ( with respect to the total number of characters in the corpus ) . Our experiments on English , Chinese and Greek datasets show that SVM with key-substring-group features can achieve outstanding performance for various text classification tasks .
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