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Applying the Multiple Cause Mixture Model to Text Categorization (1996) [19 citations — 0 self]

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@MISC{Sahami96applyingthe,
    author = {Mehran Sahami and Marti Hearst and Eric Saund},
    title = {Applying the Multiple Cause Mixture Model to Text Categorization},
    year = {1996}
}

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Abstract:

This paper introduces the use of the Multiple Cause Mixture Model to automatic text category assignment. Although much research has been done on text categorization, this algorithm is novel in that is unsupervised, that is, does not require pre-labeled training examples, and it can assign multiple category labels to documents. In this paper we present very preliminary results of the application of this model to a standard test collection, evaluating it in supervised mode in order to facilitate comparison with other methods, and showing initial results of its use in unsupervised mode. Introduction The popularity of searching the contents of the Internet has recently increased recognition of the need for automatic assignment of category labels to documents in large text collections. Web interfaces such as Stanford 's Yahoo web search system (Yahoo! 1995) make use of manually-assigned category labels to help users understand the structure of its text collection. However, manual informati...

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