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- W2103292002 abstract "We present a novel probabilistic classifier, which scales well to problems that involve a large number of classes and require training on large datasets. A prominent example of such a problem is language modeling. Our classifier is based on the assumption that each feature is associated with a predictive strength, which quantifies how well the feature can predict the class by itself. The predictions of individual features can then be combined according to their predictive strength, resulting in a model, whose parameters can be reliably and efficiently estimated. We show that a generative language model based on our classifier consistently matches modified Kneser-Ney smoothing and can outperform it if sufficiently rich features are incorporated." @default.
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- W2103292002 date "2011-06-19" @default.
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- W2103292002 title "A Scalable Probabilistic Classifier for Language Modeling" @default.
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