Multilingual Part-of-Speech Tagging Two Unsupervised Approaches
Name
Naseem-2009-Multilingual Part-of-Speech Tagging Two Unsupervised Approaches.pdf
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1.34 MB
Format
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Author(s)
Naseem, Tahira
Snyder, Benjamin
Eisenstein, Jacob
Barzilay, Regina
Date Issued
November 2009
Journal
Journal of Artificial Intelligence Research
Publisher
AI Access Foundation
Citation
Naseem, Tahira, et al. "Multilingual Part-of-Speech Tagging Two Unsupervised Approaches." Journal of Artificial Intelligence Research 36 (2009) 341-385. © AI Access Foundation.
Version
Final published version
Abstract
We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging.
The central assumption of our work is that by combining cues from multiple languages, the
structure of each becomes more apparent. We consider two ways of applying this intuition to the
problem of unsupervised part-of-speech tagging: a model that directly merges tag structures for
a pair of languages into a single sequence and a second model which instead incorporates multilingual
context using latent variables. Both approaches are formulated as hierarchical Bayesian
models, using Markov Chain Monte Carlo sampling techniques for inference. Our results demonstrate
that by incorporating multilingual evidence we can achieve impressive performance gains
across a range of scenarios. We also found that performance improves steadily as the number of
available languages increases.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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