Paper: Overfitting Avoidance For Stochastic Modeling Of Attribute-Value Grammars

ACL ID W00-0709
Title Overfitting Avoidance For Stochastic Modeling Of Attribute-Value Grammars
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2000
Authors

We present a novel approach to the problem of overfitting in the training of stochastic mod- els 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 orig- inal models, and contain fewer parameters. Em- pirical 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.