Abstract

We argue that some of the computational complexity associated with estimation of stochastic attribute- value grammars can be reduced by training upon an informative subset of the full training set. Results using the t)arsed Wall Street Journal tort)us show that in some circumstances, it is possible to obtain better estimation results using au inbrmative sampie than when training upon all the available naterial. Further experimentation demonstrates that with unlexicalised models, a Gaussian prior can reduce overfitting. However, when models are lexicalised and contain overlapping features, overfitting does not seem to be a problem, and a Gaussian prior makes minimal difference to performance. Our approach is applicable for situations when there are an infeasibly large number of parses in the training set, or else Ibr when recovery of these parses fi'om a packed representation is itself comi)utationally expensive.

Citations