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Robert C. Berwick

Robert C. Berwick is Professor of Computational Linguistics and Computer Science and Engineering, in the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society at MIT and the author of Computational Complexity and Natural Language and The Acquisition of Syntactic Knowledge, both published by the MIT Press.

Titles by This Author

Language and Evolution

“A loosely connected collection of four essays that will fascinate anyone interested in the extraordinary phenomenon of language.”
New York Review of Books

Computational Complexity and Natural Language heralds an entirely new way of looking at grammatical systems. It applies the recently developed computer science tool of complexity theory to the study of natural language.

This landmark work in computational linguistics is of great importance both theoretically and practically because it shows that much of English grammar can be learned by a simple program.The Acquisition of Syntactic Knowledge investigates the central questions of human and machine cognition: How do people learn language? How can we get a machine to learn language?

Language Use and Acquisition

Written primarily from the perspective of computational theory, Grammatical Basis of Linguistic Performance presents a synthesis of some major recent developments in grammatical theory and its application to models of language performance. Its main thesis is that Chomsky's government-binding theory is a good foundation for models of both machine parsing and language learnability.Both authors are at MIT. Robert C. Berwick is Assistant Professor in the Department of Electrical Engineering and Computer Science, and Amy Weinberg is in the Department of Linguistics and Philosophy.

Titles by This Editor

As the contributions to this book make clear, a fundamental change is taking place in the study of computational linguistics analogous to that which has taken place in the study of computer vision over the past few years and indicative of trends that are likely to affect future work in artificial intelligence generally.The first wave of efforts on machine translation and the formal mathematical study of parsing yielded little real insight into how natural language could be understood by computers or how computers could lead to an understanding of natural language.