Learning Timeline Difference for Text Categorization

Abstract

This paper addresses text categorization problem that training data may derive from a different time period from the test data. We present a learning framework which extends a boosting technique to learn accurate model for timeline adaptation. The results showed that the method was comparable to the current state-of-theart biased-SVM method, especially the method is effective when the creation time period of the test data differs greatly from the training data.