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 parsed Wall
Street Journal corpus show that in some circumstances, it is possible to obtain
better estimation results using an informative sample than when training upon
all the available material. 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 for when
recovery of these parses from a packed representation is itself computationally
expensive.Comment: 6 pages, 2 figures. Coling 2000, Saarbr\"{u}cken, Germany. pp
586--59