Abstract:Traditional text classification methods make a basic assumption: the training and test set are homologous, while this naïve assumption may not hold in the real world, especially in the web environment. Documents on the web change from time to time, pre-trained model may be out of date when applied to new emerging documents. However some information of training set is nonetheless useful. In this paper we proposed a novel method to discover the constant common knowledge in both training and test set by transfer … Show more
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