Probabilistic Topic Modeling in Multilingual Settings: A Short Overview of Its Methodology and Applications
published: Jan. 11, 2013, recorded: December 2012, views: 5335
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Probabilistic topic models are unsupervised generative models that 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. Novel topic models have been designed to work with parallel and comparable texts. We define the concept of multilingual probabilistic topic modeling and present a short high-level overview of the current research and methodology. As a representative example, we thoroughly describe a multilingual probabilistic topic model called bilingual LDA (BiLDA) trained on comparable data in the appendix. In the paper we provide a short overview of cross-lingual applications for which we utilized the model in our research so far.
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