  Log-linear models provide a statistically sound framework for Stochastic
``Unification-Based'' Grammars (SUBGs) and stochastic versions of other kinds
of grammars. We describe two computationally-tractable ways of estimating the
parameters of such grammars from a training corpus of syntactic analyses, and
apply these to estimate a stochastic version of Lexical-Functional Grammar.
