Leveraging Web 2.0 Sources for Web Content Classification
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
International Journal of Human-Computer Studies
Automatic content-based categorization of Wikipedia articles
People's Web '09 Proceedings of the 2009 Workshop on The People's Web Meets NLP: Collaboratively Constructed Semantic Resources
Concept labeling: building text classifiers with minimal supervision
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Hi-index | 0.00 |
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.