Research on text classification based on combining LSI with SVM

In traditional vector space modal, key words are used to represent the text, but the problems ofpolysemy and synonymy are not taken into account. To solve the problem, a text classification method combining latent semantic indexing with support vector machine is presented, using latent semantic indexing to obtain latent semantic structure of original feature vector. The experimental result shows that comparing to using the SVM solely, the dimension of feature vector drops largely with the accuracy of this method dropping a little.