Multilingual Topic Models for Unaligned Text

Jordan Boyd-Graber; David Blei

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arXiv · 1205.2657

Multilingual Topic Models for Unaligned Text

Jordan Boyd-Graber·David Blei

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Abstract

We develop the multilingual topic model for unaligned text (MuTo), a probabilistic model of text that is designed to analyze corpora composed of documents in two languages. From these documents, MuTo uses stochastic EM to simultaneously discover both a matching between the languages and multilingual latent topics. We demonstrate that MuTo is able to find shared topics on real-world multilingual corpora, successfully pairing related documents across languages. MuTo provides a new framework for creating multilingual topic models without needing carefully curated parallel corpora and allows applications built using the topic model formalism to be applied to a much wider class of corpora.

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BibTeX RIS

Jordan Boyd-Graber, David Blei. 2012-05-09. Multilingual Topic Models for Unaligned Text. https://arxiv.org/abs/1205.2657

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