Foundations of statistical natural language processing
Foundations of statistical natural language processing
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
From baby steps to Leapfrog: how "Less is More" in unsupervised dependency parsing
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Fast unsupervised dependency parsing with arc-standard transitions
ROBUS-UNSUP '12 Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP
Capitalization cues improve dependency grammar induction
WILS '12 Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure
Three dependency-and-boundary models for grammar induction
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 examine the utility of a curriculum (a means of presenting training samples in a meaningful order) in unsupervised learning of probabilistic grammars. We introduce the incremental construction hypothesis that explains the benefits of a curriculum in learning grammars and offers some useful insights into the design of curricula as well as learning algorithms. We present results of experiments with (a) carefully crafted synthetic data that provide support for our hypothesis and (b) natural language corpus that demonstrate the utility of curricula in unsupervised learning of probabilistic grammars.