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
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Unsupervised induction of stochastic context-free grammars using distributional clustering
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Contrastive estimation: training log-linear models on unlabeled data
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Bidirectional inference with the easiest-first strategy for tagging sequence data
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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
Evaluating unsupervised part-of-speech tagging for grammar induction
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
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
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
Painless unsupervised learning with features
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Improved unsupervised POS induction through prototype discovery
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
SVD and clustering for unsupervised POS tagging
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Improved unsupervised POS induction using intrinsic clustering quality and a Zipfian constraint
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Unsupervised Russian POS tagging with appropriate context
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
Controlling complexity in part-of-speech induction
Journal of Artificial Intelligence Research
Learning syntactic categories using paradigmatic representations of word context
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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We present a novel approach to distributionalonly, fully unsupervised, POS tagging, based on an adaptation of the EM algorithm for the estimation of a Gaussian mixture. In this approach, which we call Latent-Descriptor Clustering (LDC), word types are clustered using a series of progressively more informative descriptor vectors. These descriptors, which are computed from the immediate left and right context of each word in the corpus, are updated based on the previous state of the cluster assignments. The LDC algorithm is simple and intuitive. Using standard evaluation criteria for unsupervised POS tagging, LDC shows a substantial improvement in performance over state-of-the-art methods, along with a several-fold reduction in computational cost.