A study with multi-word feature with text classification


  • Zhang Wen
  • Taketoshi Yoshida
  • Xijin Tang


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


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.



How to Cite

Wen, Z., Yoshida, T., & Tang, X. (2007). A study with multi-word feature with text classification. Proceedings of the 51st Annual Meeting of the ISSS - 2007, Tokyo, Japan, 51(2). Retrieved from https://journals.isss.org/index.php/proceedings51st/article/view/556



Chance, Discovery and Meta-synthesis