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- W2356048481 abstract "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." @default.
- W2356048481 created "2016-06-24" @default.
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- W2356048481 date "2005-01-01" @default.
- W2356048481 modified "2023-09-27" @default.
- W2356048481 title "Semi-Supervised Text Categorization Using Bootstrapping" @default.
- W2356048481 hasPublicationYear "2005" @default.
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