Multilingual Topic Models for Unaligned Text
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.
Keywords
Cite
@article{arxiv.1205.2657,
title = {Multilingual Topic Models for Unaligned Text},
author = {Jordan Boyd-Graber and David Blei},
journal= {arXiv preprint arXiv:1205.2657},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)