Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Search-based structured prediction
Machine Learning
Unsupervised search-based structured prediction
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Incrementality in deterministic dependency parsing
IncrementParsing '04 Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together
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
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
Unsupervised induction of tree substitution grammars for dependency parsing
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
Covariance in Unsupervised Learning of Probabilistic Grammars
The Journal of Machine Learning Research
Posterior Sparsity in Unsupervised Dependency Parsing
The Journal of Machine Learning Research
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
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
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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Unsupervised dependency parsing is one of the most challenging tasks in natural languages processing. The task involves finding the best possible dependency trees from raw sentences without getting any aid from annotated data. In this paper, we illustrate that by applying a supervised incremental parsing model to unsupervised parsing; parsing with a linear time complexity will be faster than the other methods. With only 15 training iterations with linear time complexity, we gain results comparable to those of other state of the art methods. By employing two simple universal linguistic rules inspired from the classical dependency grammar, we improve the results in some languages and get the state of the art results. We also test our model on a part of the ongoing Persian dependency treebank. This work is the first work done on the Persian language.