The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd 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
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
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
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
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
Using universal linguistic knowledge to guide grammar induction
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Posterior Sparsity in Unsupervised Dependency Parsing
The Journal of Machine Learning Research
Why initialization matters for IBM model 1: multiple optima and non-strict convexity
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Punctuation: making a point in unsupervised dependency parsing
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Unsupervised dependency parsing without gold part-of-speech tags
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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We investigate models for unsupervised learning with concave log-likelihood functions. We begin with the most well-known example, IBM Model 1 for word alignment (Brown et al., 1993) and analyze its properties, discussing why other models for unsupervised learning are so seldom concave. We then present concave models for dependency grammar induction and validate them experimentally. We find our concave models to be effective initializers for the dependency model of Klein and Manning (2004) and show that we can encode linguistic knowledge in them for improved performance.