Learning attribute values in typed-unification grammars: On generalised rule reduction

We present Generalised Reduction (GR), a learning technique for generalising attribute/feature values in typed-unification grammars. GR eliminates as many as possible of the feature constraints (FCs) from the type feature structures (FSs) while applying a criterion of preserving the parsing results on a given, training corpus. For parsing with GR-restricted rule FSs, and for checking the correctness of obtained parses on other corpora, one may use a new form FS unification which we call two-step unication to speed up parsing. We report results on a large-scale HPSG grammar for English