Semi-Supervised Text Categorization Using Bootstrapping
Wen Chen · Zhongwen xinxi xuebao · 2005
This paper proposes a semi supervised text categorization using bootstrapping. The System uses the Maximum Entropy Model as the text classifier. It learns more automatic labeled samples as new seed training samples from unlabeled samples using a small size of seed training samples. In this paper, we use a weighted factor to adjust the weight of new seed samples during the following training process. The experimental results show that the proposed system performs better than the conventional system with the same labeled documents. And it yields 70 56% F1 using only 100 labeled documents for each category, 4 7% over the conventional system does. And it can provide the same performance as the conventional system using 50% or less training samples. The results also show that the weighted factor can improve the performance.