Year: 1994
Type: conference-paper
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.
Authors: Scott Miller, Richard Schwartz, Robert Bobrow, Robert Ingria
Institution: Northeastern University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2046677175