Probabilistic Models in the Study of Language
by Roger Levy
Publisher: University of California, San Diego 2012
Number of pages: 274
A textbook on the topic of using probabilistic models in scientific work on language ranging from experimental data analysis to corpus work to cognitive modeling. The intended audience is graduate students in linguistics, psychology, cognitive science, and computer science who are interested in using probabilistic models to study language.
Home page url
Download or read it online for free here:
by A. Aliseda, R. van Glabbeek, D. Westerstahl - CSLI
This book pursues the recent research in the interface of logic, language and computation, with applications to artificial intelligence and machine learning. It contains contributions to the logical and computational analysis of natural language.
by 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.
by Igor Boshakov, Alexander Gelbukh
The book focuses on the basic set of ideas and facts from the fundamental science necessary for the creation of intelligent language processing tools, without going deeply into the details of specific algorithms or toy systems.
by Gerald Gazdar, Chris Mellish - Addison-Wesley
The major focus of this book is on the processing of the orthographic forms of natural language utterances and text. Most of the book deals with the parsing and understanding of natural language, much less on the production of it.