Language, Proof and Logic
by Jon Barwise, John Etchemendy
Publisher: Center for the Study of Language 2011
ISBN/ASIN: 157586374X
ISBN-13: 9781575863740
Number of pages: 620
Description:
This textbook/software package covers first-order language in a method appropriate for first and second courses in logic. The unique on-line grading services instantly grades solutions to hundred of computer exercises. It is specially devised to be used by philosophy instructors in a way that is useful to undergraduates of philosophy, computer science, mathematics, and linguistics. Advanced chapters include proofs of soundness and completeness for propositional and predicate logic, as well as an accessible sketch of Godel's first incompleteness theorem.
Download or read it online for free here:
Download link
(2.5MB, PDF)
Similar books
Natural Language Processing with Pythonby Steven Bird, Ewan Klein, Edward Loper - O'Reilly Media
This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies. With it, you'll learn how to write Python programs that work with large collections of unstructured text.
(14718 views)
Natural Language Processing for Prolog Programmersby Michael A. Covington - Prentice-Hall
Designed to bridge the gap for those who know Prolog but have no background in linguistics, this book concentrates on turning theories into practical techniques. Coverage includes template and keyword systems, definite clause grammars, and more.
(8723 views)
Speech and Language Processingby Dan Jurafsky, James H. Martin - Stanford University
This text takes an empirical approach to the subject, based on applying statistical and machine-learning algorithms to large corporations. The authors describe a unified vision of speech and language processing. Emphasis is on practical applications.
(5180 views)
A Maximum Entropy Approach to Natural Language Processingby A. L. Berger, S. A. Della Pietra, V. J. Della Pietra - Association for Computational Linguistics
The authors describe a method for statistical modeling based on maximum entropy. They present a maximum-likelihood approach for automatically constructing maximum entropy models and describe how to implement this approach efficiently.
(8160 views)