Numerical continuation methods: an introduction
Numerical continuation methods: an introduction
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Robot Shaping: An Experiment in Behavior Engineering
Robot Shaping: An Experiment in Behavior Engineering
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Tagging English text with a probabilistic model
Computational Linguistics
Does Baum-Welch re-estimation help taggers?
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
The unsupervised learning of natural language structure
The unsupervised learning of natural language structure
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Annealing techniques for unsupervised statistical language learning
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Annealing structural bias in multilingual weighted grammar induction
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Effective self-training for parsing
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Multilevel coarse-to-fine PCFG parsing
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
The Unreasonable Effectiveness of Data
IEEE Intelligent Systems
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Coarse-to-fine syntactic machine translation using language projections
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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
Improving unsupervised dependency parsing with richer contexts and smoothing
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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
An empirical study of semi-supervised structured conditional models for dependency parsing
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Coarse-to-fine natural language processing
Coarse-to-fine natural language processing
Word representations: a simple and general method for semi-supervised learning
ACL '10 Proceedings of the 48th Annual Meeting 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
A multi-pass sieve for coreference resolution
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Improved fully unsupervised parsing with zoomed learning
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Unsupervised induction of tree substitution grammars for dependency parsing
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
It depends on the translation: unsupervised dependency parsing via word alignment
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Covariance in Unsupervised Learning of Probabilistic Grammars
The Journal of Machine Learning Research
Inducing Tree-Substitution Grammars
The Journal of Machine Learning Research
Posterior Sparsity in Unsupervised Dependency Parsing
The Journal of Machine Learning Research
Neutralizing linguistically problematic annotations in unsupervised dependency parsing evaluation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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
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
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
Concavity and initialization for unsupervised dependency parsing
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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
Smoothing for bracketing induction
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Deterministic coreference resolution based on entity-centric, precision-ranked rules
Computational Linguistics
Bayesian Constituent Context Model for Grammar Induction
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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We present three approaches for unsupervised grammar induction that are sensitive to data complexity and apply them to Klein and Manning's Dependency Model with Valence. The first, Baby Steps, bootstraps itself via iterated learning of increasingly longer sentences and requires no initialization. This method substantially exceeds Klein and Manning's published scores and achieves 39.4% accuracy on Section 23 (all sentences) of the Wall Street Journal corpus. The second, Less is More, uses a low-complexity subset of the available data: sentences up to length 15. Focusing on fewer but simpler examples trades off quantity against ambiguity; it attains 44.1% accuracy, using the standard linguistically-informed prior and batch training, beating state-of-the-art. Leapfrog, our third heuristic, combines Less is More with Baby Steps by mixing their models of shorter sentences, then rapidly ramping up exposure to the full training set, driving up accuracy to 45.0%. These trends generalize to the Brown corpus; awareness of data complexity may improve other parsing models and unsupervised algorithms.