Partially Supervised Text Classification with Multi-Level Examples


  • Tao Liu Renmin University of China
  • Xiaoyong Du Renmin University of China
  • Yongdong Xu Harbin Institute of Technology
  • Minghui Li Microsoft
  • Xiaolong Wang Harbin Institute of Technology


Partially supervised text classification has received great research attention since it only uses positive and unlabeled examples as training data. This problem can be solved by automatically labeling some negative (and more positive) examples from unlabeled examples before training a text classifier. But it is difficult to guarantee both high quality and quantity of the new labeled examples. In this paper, a multi-level example based learning method for partially supervised text classification is proposed, which can make full use of all unlabeled examples. A heuristic method is proposed to assign possible labels to unlabeled examples and partition them into multiple levels according to their labeling confidence. A text classifier is trained on these multi-level examples using weighted support vector machines. Experiments show that the multi-level example based learning method is effective for partially supervised text classification, and outperforms the existing popular methods such as Biased-SVM, ROC-SVM, S-EM and WL.




How to Cite

Liu, T., Du, X., Xu, Y., Li, M., & Wang, X. (2011). Partially Supervised Text Classification with Multi-Level Examples. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 890-895. Retrieved from



AAAI Technical Track: Natural Language Processing