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Lecture Notes in Computer Science
Probabilistic topic models are a group of unsupervised generative machine learning models that can be effectively trained on large text collections. They model document content as a two-step generation process, i.e., documents are observed as mixtures of latent topics, while topics are probability distributions over vocabulary words. Recently, a significant research effort has been invested into transferring the probabilistic topic modeling concept from monolingual to multilingual settings.doi:10.1007/978-3-642-36973-5_106 fatcat:bnk7t6l3zjfmval53l3lu5bbym