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Boosting Inductive Transfer for Text Classification using Wikipedia

Banerjee, Somnath

HPL-2008-42

Keyword(s): Inductive Transfer, Text Classification, Wikipedia

Abstract: Inductive transfer is applying knowledge learned on one set of tasks to improve the performance of learning a new task. Inductive transfer is being applied in improving the generalization performance on a classification task using the models learned on some related tasks. In this paper, we show a method of making inductive transfer for text classification more effective using Wikipedia. We map the text documents of the different tasks to a feature space created using Wikipedia, thereby providing some background knowledge of the contents of the documents. It has been observed here that when the classifiers are built using the features generated from Wikipedia they become more effective in transferring knowledge. An evaluation on the daily classification task on the Reuters RCV1 corpus shows that our method can significantly improve the performance of inductive transfer. Our method was also able to successfully overcome a major obstacle observed in a recent work on a similar setting. Publication Info: Published and presented at ICMLA 2007, the Sixth International Conference on Machine Learning and Applications (ICMLA'07), 13-15 Dec. 2007 Cincinnati, Ohio, USA

6 Pages

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