Class-based n-gram models of natural language
Computational Linguistics
Algorithms for bigram and trigram word clustering
Speech Communication
Tagging English text with a probabilistic model
Computational Linguistics
Distributional part-of-speech tagging
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Combining distributional and morphological information for part of speech induction
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Annealing techniques for unsupervised statistical language learning
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Contrastive estimation: training log-linear models on unlabeled data
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Toward unsupervised whole-corpus tagging
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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
Comparing clusterings---an information based distance
Journal of Multivariate Analysis
Automatic Evaluation of Information Ordering: Kendall's Tau
Computational Linguistics
Unsupervised part-of-speech tagging employing efficient graph clustering
COLING ACL '06 Proceedings of the 21st International Conference on computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
The NVI clustering evaluation measure
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
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
Unsupervised multilingual learning for POS tagging
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Minimized models for unsupervised part-of-speech tagging
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
The infinite HMM for unsupervised PoS tagging
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
A simple unsupervised learner for POS disambiguation rules given only a minimal lexicon
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Improved unsupervised POS induction through prototype discovery
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Crouching Dirichlet, hidden Markov model: unsupervised POS tagging with context local tag generation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Latent-descriptor clustering for unsupervised POS induction
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Confidence driven unsupervised semantic parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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Modern unsupervised POS taggers usually apply an optimization procedure to a non-convex function, and tend to converge to local maxima that are sensitive to starting conditions. The quality of the tagging induced by such algorithms is thus highly variable, and researchers report average results over several random initializations. Consequently, applications are not guaranteed to use an induced tagging of the quality reported for the algorithm. In this paper we address this issue using an unsupervised test for intrinsic clustering quality. We run a base tagger with different random initializations, and select the best tagging using the quality test. As a base tagger, we modify a leading unsupervised POS tagger (Clark, 2003) to constrain the distributions of word types across clusters to be Zipfian, allowing us to utilize a perplexity-based quality test. We show that the correlation between our quality test and gold standard-based tagging quality measures is high. Our results are better in most evaluation measures than all results reported in the literature for this task, and are always better than the Clark average results.