  The paper presents a language model that develops syntactic structure and
uses it to extract meaningful information from the word history, thus enabling
the use of long distance dependencies. The model assigns probability to every
joint sequence of words--binary-parse-structure with headword annotation and
operates in a left-to-right manner --- therefore usable for automatic speech
recognition. The model, its probabilistic parameterization, and a set of
experiments meant to evaluate its predictive power are presented; an
improvement over standard trigram modeling is achieved.
