Adding More Languages Improves Unsupervised Multilingual Part-of-Speech Tagging: A Bayesian Non-Parametric Approach

Adding More Languages Improves Unsupervised Multilingual Part-of-Speech Tagging: A Bayesian Non-Parametric Approach

We investigate the problem of unsupervised part-of-speech tagging when raw parallel data is available in a large number of languages. Patterns of ambiguity vary greatly across languages and therefore even unannotated multilingual data can serve as a learning signal. We propose a non-parametric Bayes...

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Bibliographic Details
Main Authors: Snyder, Benjamin, Naseem, Tahira, Eisenstein, Jacob, Barzilay, Regina
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:en_US
Published: Association for Computational Linguistics 2010
Online Access:http://hdl.handle.net/1721.1/58926
https://orcid.org/0000-0002-2921-8201

Internet

http://hdl.handle.net/1721.1/58926
https://orcid.org/0000-0002-2921-8201

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