Logo

Computational Linguistics by Igor Boshakov, Alexander Gelbukh

Small book cover: Computational Linguistics

Computational Linguistics
by


ISBN/ASIN: 9703601472
Number of pages: 198

Description:
The contents of the book are based on the course on computational linguistics that has been delivered by the authors since 1997 at the Center for Computing Research, National Polytechnic Institute, Mexico City. 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.

Home page url

Download or read it online for free here:
Download link
(1.6MB, PDF)

Similar books

Book cover: How Mobile Robots Can Self-organise a VocabularyHow Mobile Robots Can Self-organise a Vocabulary
by - Language Science Press
This book presents a series of experiments in which two robots try to solve the symbol grounding problem. The experiments are based on the language game paradigm, and involve real mobile robots that are able to develop a grounded lexicon ...
(3972 views)
Book cover: Natural Language Processing with PythonNatural Language Processing with Python
by - 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.
(13658 views)
Book cover: Probabilistic Models in the Study of LanguageProbabilistic Models in the Study of Language
by - University of California, San Diego
A book 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 and computer science.
(4453 views)
Book cover: A Maximum Entropy Approach to Natural Language ProcessingA Maximum Entropy Approach to Natural Language Processing
by - 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.
(7363 views)