  Phrase-structure grammars are effective models for important syntactic and
semantic aspects of natural languages, but can be computationally too demanding
for use as language models in real-time speech recognition. Therefore,
finite-state models are used instead, even though they lack expressive power.
To reconcile those two alternatives, we designed an algorithm to compute
finite-state approximations of context-free grammars and
context-free-equivalent augmented phrase-structure grammars. The approximation
is exact for certain context-free grammars generating regular languages,
including all left-linear and right-linear context-free grammars. The algorithm
has been used to build finite-state language models for limited-domain speech
recognition tasks.
