Distributional part-of-speech tagging
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inducing syntactic categories by context distribution clustering
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Toward unsupervised whole-corpus tagging
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
An incremental bayesian model for learning syntactic categories
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
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
Online entropy-based model of lexical category acquisition
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
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
Two decades of unsupervised POS induction: how far have we come?
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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We introduce Conflict-Driven Co-Clustering, a novel algorithm for data co-clustering, and apply it to the problem of inducing parts-of-speech in a corpus of child-directed spoken English. Co-clustering is preferable to unidimensional clustering as it takes into account both item and context ambiguity. We show that the categorization performance of the algorithm is comparable with the co-clustering algorithm of Leibbrandt and Powers (2008), but out-performs that algorithm in robustly pruning less-useful clusters and merging them into categories strongly corresponding to the three main open classes of English.