A maximum entropy approach to natural language processing
Computational Linguistics
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A probability model to improve word alignment
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
A maximum entropy model for prepositional phrase attachment
HLT '94 Proceedings of the workshop on Human Language Technology
Phrasal cohesion and statistical machine translation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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The study presented in this article is dedicated to a syntactic parser for Romanian. The central goal of the presented technique is to learn a model which is able to discriminate between probability for a word to be head of another word in a dependency structure corresponding to a sentence in the considered language. The model described in this paper was trained on a dependency treebank linguistic resource and is intended to be used in order to develop a dependency syntactic parser.