Statistical Language Processing Using Hidden Understanding Models

Scott Miller, Richard M. Schwartz, Robert J. Bobrow, Robert Ingria

Abstract: This paper introduces a class of statistical mechanisms, called hidden understanding models, for natural language processing. Much of the framework for hidden understanding models derives from statistical models used in speech recognition, especially the use of hidden Markov models. These techniques are applied to the central problem of determining meaning directly from a sequence of spoken or written words. We present an overall description of the hidden understanding methodology, and discuss some of the critical implementation issues. Finally, we report on experimental results, including results of the December 1993 ARPA evaluation.