We present an algorithm for computing n-gram probabilities from stochastic context-free grammars, a procedure that can alleviate some of the standard problems associated with n-grams (estimation from sparse data, lack of linguistic structure, among others). The method operates via the computation of substring expectations, which in turn is accomplished by solving systems of linear equations derived from the grammar. The procedure is fully implemented and has proved viable and useful in practice.

This paper describes the architecture and performance of the Berkeley Restaurant Project (BeRP), a medium-vocabulary, speaker-independent, spontaneous continuous speech understanding system currently under development at ICSI. BeRP serves as a testbed for a number of our speech-related research projects, including robust feature extraction, connectionist phonetic likelihood estimation, automatic induction of multiplepronunciation lexicons, foreign accent detection and modeling, advanced language models, and lip-reading. In addition, it has proved quite usable in its function as a database frontend, even though many of our subjects are non-native speakers of English. 1 OVERVIEW The BeRP system functions as a knowledge consultant whose domain is restaurants in the city of Berkeley, California. As a knowledge consultant, it draws inspiration from earlier consultants like VOYAGER [15]. Users ask spoken language questions of BeRP, which directs questions to the user and then queries a dat...

Efficient knowledge representation and reasoning is an important component of intelligent activity, and is a crucial aspect in the design of large-scale intelligent systems. This dissertation explores the design, analysis, and implementation of massively parallel knowledge representation and reasoning systems which can encode very large knowledge bases and respond to a class of queries in real-time, with reasoning episodes expected to span a fraction of a second. The dissertation attempts to design efficient, large-scale knowledge base systems by: (i) exploiting massive parallelism; and (ii) constraining representational and inferential capabilities to achieve tractability, while still retaining sufficient expressive power to capture a broad class of reasoning in intelligent systems. To this end, shruti, a connectionist reasoning system which models reflexive--- i.e., effortless and spontaneous---reasoning serves as the knowledge representation and reasoning framework. Shruti-based mas...

We map structured connectionist models of knowledge representation and reasoning onto existing general purpose massively parallel architectures with the objective of developing and implementing practical, real-time reasoning systems. Shruti, a connectionist knowledge representation and reasoning system which attempts to model reflexive reasoning, serves as our representative connectionist model. Realizations of shruti are developed on the Connection Machine CM-2---an SIMD architecture---and on the Connection Machine CM5 ---an MIMD architecture. Though SIMD implementations on the CM-2 are reasonably fast---requiring a few seconds to tens of seconds for answering queries---experiments indicate that SPMD message passing systems are vastly superior to SIMD systems and offer hundred-fold speedups. The CM-5 implementation can encode large knowledge bases with several hundred thousand (randomly generated) rules and facts, and respond in under 500 milliseconds to a range of queries requiring i...

This paper describes the architecture and performance of the Berkeley Restaurant Project (BeRP), a medium-vocabulary, speaker-independent, spontaneous continuous speech understanding system currently under development at ICSI. BeRP serves as a testbed for a number of our speech-related research projects, including robust feature extraction, connectionist phonetic likelihood estimation, automatic induction of multiplepronunciation lexicons, foreign accent detection and modeling, advanced language models, and lip-reading. In addition, it has proved quite usable in its function as a database frontend, even though many of our subjects are non-native speakers of English. 1 OVERVIEW The BeRP system functions as a knowledge consultant whose domain is restaurants in the city of Berkeley, California. As a knowledge consultant, it draws inspiration from earlier consultants like VOYAGER [15]. Users ask spoken language questions of BeRP, which directs questions to the user and then queries a dat...