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• W1878153963 abstract "Topic models traditionally rely on the bag-of-words assumption. In data mining applications, this often results in end-users being presented with inscrutable lists of topical unigrams, single words inferred as representative of their topics. In this article, we present a hierarchical generative probabilistic model of topical phrases. The model simultaneously infers the location, length, and topic of phrases within a corpus and relaxes the bag-of-words assumption within phrases by using a hierarchy of Pitman-Yor processes. We use Markov chain Monte Carlo techniques for approximate inference in the model and perform slice sampling to learn its hyperparameters. We show via an experiment on human subjects that our model finds substantially better, more interpretable topical phrases than do competing models." @default.
• W1878153963 created "2016-06-24" @default.
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• W1878153963 date "2012-07-12" @default.
• W1878153963 modified "2023-09-26" @default.
• W1878153963 title "A Phrase-Discovering Topic Model Using Hierarchical Pitman-Yor Processes" @default.
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