A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
The adaptation of a machine-learned sentence realization system to French
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Machine-learned contexts for linguistic operations in German sentence realization
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The impact of parse quality on syntactically-informed statistical machine translation
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Dependency-based n-gram models for general purpose sentence realisation
Natural Language Engineering
Generating non-projective word order in statistical linearization
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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We profile the occurrence of clausal extraposition in corpora from different domains and demonstrate that extraposition is a pervasive phenomenon in German that must be addressed in German sentence realization. We present two different approaches to the modeling of extraposition, both based on machine learned decision tree classifiers. The two approaches differ in their view of the movement operation: one approach models multi-step movement through intermediate nodes to the ultimate target node, while the other approach models one-step movement to the target node. We compare the resulting models, trained on data from two domains and discuss the differences between the two types of models and between the results obtained in the different domains.