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Abstract: . This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large quantities of unlabeled documents are readily available. We introduce an algorithm for learning from labeled and unlabeled documents based on the combination of Expectation-Maximization (EM) and a naive ... (Update)
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.... as a way to utilize the relatively small amount of available annotated data along with much larger collections of unannotated data [1,9]. However, it is as yet unclear whether these methods are effective other than in cases where we have relatively small amounts of...
.... proposed in [29] 4 Related Work Many methods have been proposed for text classification (e.g. [22, 21, 15, 6, 11, 12, 5, 31, 25, 28, 13, 3, 9, 14, 16, 24, 10, 27, 32, 17, 23]) We describe here two typical non rule based methods and two typical rule based method. We also...
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BibTeX entry: (Update)
Nigam, K.; McCallum, A.; Thrun, S.; and Mitchell, T. 1999. Text classification from labeled and unlabeled documents using EM. Machine Learning. To appear. http://citeseer.ist.psu.edu/nigam99text.html More
@article{ nigam00text,
author = "Kamal Nigam and Andrew K. McCallum and Sebastian Thrun and Tom M. Mitchell",
title = "Text Classification from Labeled and Unlabeled Documents using {EM}",
journal = "Machine Learning",
volume = "39",
number = "2/3",
pages = "103--134",
year = "2000",
url = "citeseer.ist.psu.edu/nigam99text.html" }
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