The paper presents a language model that develops syntactic structure and uses it to extract nmaningflfl 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 mannertherefore usable for automatic speech recognition. The model, its probabilistic parameterization, and a set of experinmnts meant to evaluate its predictive power are presented; an improvement over standard trigram modeling is achieved.