Briefly noted - English for the computer: the SUSANNE corpus and analytic scheme
Computational Linguistics
Cubic-time Parsing and Learning Algorithms for Grammatical Bigram
Cubic-time Parsing and Learning Algorithms for Grammatical Bigram
Discovery of linguistic relations using lexical attraction
Discovery of linguistic relations using lexical attraction
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Evaluation of TnT Tagger for Spanish
ENC '03 Proceedings of the 4th Mexican International Conference on Computer Science
Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammars
Computational Linguistics - Special issue on using large corpora: I
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Inside-outside reestimation from partially bracketed corpora
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Methods for obtaining corresponding phrase structure and dependency grammars
COLING '67 Proceedings of the 1967 conference on Computational linguistics
Contribution of a category hierarchy to the robustness of syntactic parsing.
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 2
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on 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
Unsupervised grammar induction by distribution and attachment
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Multi-lingual dependency parsing at NAIST
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Multilingual dependency analysis with a two-stage discriminative parser
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Parser combination by reparsing
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Two approaches for building an unsupervised dependency parser and their other applications
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
On the complexity of non-projective data-driven dependency parsing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
On the usage of morphological tags for grammar induction
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Cat3LB and Cast3LB: from constituents to dependencies
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
NLDB'06 Proceedings of the 11th international conference on Applications of Natural Language to Information Systems
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The unsupervised approach for syntactic analysis tries to discover the structure of the text using only raw text. In this paper we explore this approach using Grammar Inference Algorithms. Despite of still having room for improvement, our approach tries to minimize the effect of the current limitations of some grammar inductors by adding morphological information before the grammar induction process, and a novel system for converting a shallow parse to dependencies, which reconstructs information about inductor's undiscovered heads by means of a lexical categories precedence system. The performance of our parser, which needs no syntactic tagged resources or rules, trained with a small corpus, is 10% below to that of commercial semisupervised dependency analyzers for Spanish, and comparable to the state of the art for English.