A study with multi-word feature with text classification

Zhang Wen, Taketoshi Yoshida, Xijin Tang


We carried out a series of experiments on text classification using multi-word features. A hand-crafted method was proposed to extract the multi-words from text data set and two different strategies were developed to normalize the multi-words into two different versions of multi-word features. After the texts were represented respectively using these two different multi-word features, text classification was conducted in contrast to examine the effectiveness of these two strategies. Also the linear and nonlinear polynomial kernel of support vector machine (SVM) was compared on the performance of text classification task.


text classification, multi-word features, feature selection, SVM

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