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Abstract: This paper shows that the accuracy of learned text classifiers can be improved by augmenting small numbers of labeled training documents with a large pool of unlabeled documents. This is significant because in many important text classification problems obtaining classification labels is expensive, while large quantities of unlabeled documents are readily available. We present a theoretical argument showing that, under common assumptions, unlabeled data contain information about the target... (Update)
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BibTeX entry: (Update)
K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. Learning to classify text from labeled and unlabeled documents. In Proceedings of the Fifteenth National Conference on Artificial Intelligence. AAAI Press, 1998. http://citeseer.ist.psu.edu/article/nigam98learning.html More
@inproceedings{ nigam98learning,
author = "Kamal Nigam and Andrew K. McCallum and Sebastian Thrun and Tom M. Mitchell",
title = "Learning to classify text from labeled and unlabeled documents",
booktitle = "Proceedings of {AAAI}-98, 15th Conference of the American Association for Artificial Intelligence",
publisher = "AAAI Press, Menlo Park, US",
address = "Madison, US",
pages = "792--799",
year = "1998",
url = "citeseer.ist.psu.edu/article/nigam98learning.html" }
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