Probabilistic analogues of regular and context-free grammars are well-known in
computational linguistics, and currently the subject of intensive research. To date, however …

Probabilistic analogues of regular and context-free grammars are well-known in
computational linguistics, and currently the subject of intensive research. To date, however …

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 …

Formal grammars are widely used in speech recognition, language translation, and
language understanding systems. Grammars rich enough to accommodate natural …

Log-linear models provide a statistically sound framework for Stochastic``Unification-
Based''Grammars (SUBGs) and stochastic versions of other kinds of grammars. We describe …

We consider several empirical estimators for probabilistic context-free grammars, and show
that the estimated grammars have the so-called consistency property, under the most …

This paper describes a method for estimating conditional probability distributions over the
parses of``unification-based''grammars which can utilize auxiliary distributions that are …

We present an efficient learning algorithm for probabilistic context-free grammars based on
the variational Bayesian approach. Although the maximum likelihood method has …

Stochastic categorial grammars (SCGs) are introduced as a more appropriate formalism for
statistical language learners to estimate than stochastic context free grammars. As a vehicle …

This paper is devoted to the estimation of stochastic context-free grammars (SCFGs) and
their use as language models. Classical estimation algorithms, together with new ones that …