Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Inducing Probabilistic Grammars by Bayesian Model Merging
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Parsing with Context-Free Grammars and Word Statistics
Parsing with Context-Free Grammars and Word Statistics
Inducing Features of Random Fields
Inducing Features of Random Fields
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Stochastic attribute-value grammars
Computational Linguistics
Automatic extraction of subcategorization from corpora
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
A probabilistic corpus-driven model for lexical-functional analysis
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Estimation of stochastic attribute-value grammars using an informative sample
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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We present a novel approach to the problem of overfitting in the training of stochastic models for selecting parses generated by attribute-valued grammars. In this approach, statistical features are merged according to the frequency of linguistic elements within the features. The resulting models are more general than the original models, and contain fewer parameters. Empirical results from the task of parse selection suggest that the improvement in performance over repeated iterations of iterative scaling is more reliable with such generalized models than with ungeneralized models.