IRI-9314969 This project intends to take advantage of the simplicity of the classical statistical trigram model of language while augmenting it with the syntactic and semantic aspects which constrain the use of the new grammatical trigram model to advantage over the purely stochastic model. The concepts of probabilistic link grammars are used in this research, incorporating trigrams into a unified framework for modeling long-distance grammatical dependencies in computationally efficient ways. The methods proposed are expected to have greater predictive power over current methods from the point of view of entropy measurements, and to integrate finite-state automata models and new statistical estimation algorithms with modern powerful machines resulting in improved speech recognition, translation, and understanding systems.
