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- W1920003194 abstract "This paper introduces the use of the Multiple Cause Mixture Model to automatic text category assign- ment. Although much research has been done on text categorization, this algorithm is novel in that is unsu- pervised, 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 or- der to facilitate comparison with other methods, and showing initial results of its use in unsupervised mode." @default.
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- W1920003194 title "Applying the Multiple Cause Mixture Model to Text Categorization" @default.
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