  This thesis presents a broad-coverage probabilistic top-down parser, and its
application to the problem of language modeling for speech recognition. The
parser builds fully connected derivations incrementally, in a single pass from
left-to-right across the string. We argue that the parsing approach that we
have adopted is well-motivated from a psycholinguistic perspective, as a model
that captures probabilistic dependencies between lexical items, as part of the
process of building connected syntactic structures. The basic parser and
conditional probability models are presented, and empirical results are
provided for its parsing accuracy on both newspaper text and spontaneous
telephone conversations. Modifications to the probability model are presented
that lead to improved performance. A new language model which uses the output
of the parser is then defined. Perplexity and word error rate reduction are
demonstrated over trigram models, even when the trigram is trained on
significantly more data. Interpolation on a word-by-word basis with a trigram
model yields additional improvements.
