Class-based n-gram models of natural language
Computational Linguistics
A systematic comparison of various statistical alignment models
Computational Linguistics
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
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
A generative constituent-context model for improved grammar induction
ACL '02 Proceedings of the 40th 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
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
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
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
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
Multilingual part-of-speech tagging: two unsupervised approaches
Journal of Artificial Intelligence Research
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
SVD and clustering for unsupervised POS tagging
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
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
Simple type-level unsupervised POS tagging
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Cat3LB and Cast3LB: from constituents to dependencies
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
A hierarchical dirichlet process model for joint part-of-speech and morphology induction
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
The PASCAL Challenge on Grammar Induction
WILS '12 Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure
Turning the pipeline into a loop: iterated unsupervised dependency parsing and PoS induction
WILS '12 Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure
Hierarchical clustering of word class distributions
WILS '12 Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure
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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In this paper we present a fully unsupervised syntactic class induction system formulated as a Bayesian multinomial mixture model, where each word type is constrained to belong to a single class. By using a mixture model rather than a sequence model (e.g., HMM), we are able to easily add multiple kinds of features, including those at both the type level (morphology features) and token level (context and alignment features, the latter from parallel corpora). Using only context features, our system yields results comparable to state-of-the art, far better than a similar model without the one-class-per-type constraint. Using the additional features provides added benefit, and our final system outperforms the best published results on most of the 25 corpora tested.