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...
Main Authors: | Snyder, Benjamin (Contributor), Naseem, Tahira (Contributor), Eisenstein, Jacob (Contributor), Barzilay, Regina (Contributor) |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor) |
Format: | Article |
Language: | English |
Published: |
Association for Computational Linguistics,
2010-10-07T13:12:43Z.
|
Subjects: | |
Online Access: | Get fulltext |
Similar Items
-
Multilingual Part-of-Speech Tagging Two Unsupervised Approaches
by: Naseem, Tahira, et al.
Published: (2011) -
Unsupervised multilingual grammar induction
by: Snyder, Benjamin, et al.
Published: (2010) -
Climbing the tower of babel: Unsupervised multilingual learning
by: Snyder, Benjamin, et al.
Published: (2011) -
Selective Sharing for Multilingual Dependency Parsing
by: Naseem, Tahira, et al.
Published: (2014) -
Unsupervised multilingual learning
by: Snyder, Benjamin, Ph. D. Massachusetts Institute of Technology
Published: (2011)