Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Inside-outside reestimation from partially bracketed corpora
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Shared logistic normal distributions for soft parameter tying in unsupervised grammar induction
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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
Profiting from mark-up: hyper-text annotations for guided parsing
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Viterbi training for PCFGs: hardness results and competitiveness of uniform initialization
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Using universal linguistic knowledge to guide grammar induction
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Simple Unsupervised Identification of Low-Level Constituents
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
Inducing Tree-Substitution Grammars
The Journal of Machine Learning Research
Simple unsupervised grammar induction from raw text with cascaded finite state models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Punctuation: making a point in unsupervised dependency parsing
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Unsupervised structure prediction with non-parallel multilingual guidance
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Multi-source transfer of delexicalized dependency parsers
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Lateen EM: unsupervised training with multiple objectives, applied to dependency grammar induction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Unsupervised dependency parsing without gold part-of-speech tags
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
On the utility of curricula in unsupervised learning of probabilistic grammars
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Fast unsupervised dependency parsing with arc-standard transitions
ROBUS-UNSUP '12 Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP
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
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 show that orthographic cues can be helpful for unsupervised parsing. In the Penn Treebank, transitions between upper- and lower-case tokens tend to align with the boundaries of base (English) noun phrases. Such signals can be used as partial bracketing constraints to train a grammar inducer: in our experiments, directed dependency accuracy increased by 2.2% (average over 14 languages having case information). Combining capitalization with punctuation-induced constraints in inference further improved parsing performance, attaining state-of-the-art levels for many languages.