Ernest P. Chan

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We apply a well-known Bayesian probabilistic model to textual information retrieval: the classification of documents based on their relevance to a query. This model was previously used with supervised training data for a fixed query. When only noisy, unsupervised training data generated from a heuristic relevance-scoring formula are available, two crucial(More)
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