Foundations of statistical natural language processing
Foundations of statistical natural language processing
Tagging English text with a probabilistic model
Computational Linguistics
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Does Baum-Welch re-estimation help taggers?
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
The Penn Chinese TreeBank: Phrase structure annotation of a large corpus
Natural Language Engineering
Building a large-scale annotated Chinese corpus
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Part of speech tagging in context
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Prototype-driven learning for sequence models
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
A comparison of Bayesian estimators for unsupervised Hidden Markov Model POS taggers
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Improving a simple bigram HMM part-of-speech tagger by latent annotation and self-training
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Adaptive Bayesian HMM for Fully Unsupervised Chinese Part-of-Speech Induction
ACM Transactions on Asian Language Information Processing (TALIP)
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We conduct a series of Part-of-Speech (POS) Tagging experiments using Expectation Maximization (EM), Variational Bayes (VB) and Gibbs Sampling (GS) against the Chinese Penn Tree-bank. We want to first establish a baseline for unsupervised POS tagging in Chinese, which will facilitate future research in this area. Secondly, by comparing and analyzing the results between Chinese and English, we highlight some of the strengths and weaknesses of each of the algorithms in POS tagging task and attempt to explain the differences based on some preliminary linguistics analysis. Comparing to English, we find that all algorithms perform rather poorly in Chinese in 1-to-1 accuracy result but are more competitive in many-to-1 accuracy. We attribute one possible explanation of this to the algorithms' inability to correctly produce tags that match the desired tag count distribution.