
Using Large Corpora
Susan Armstrong(Editor)
MIT Press
Published on 2. November 1994
Book
Paperback/Softback
359 pages
978-0-262-51082-0 (ISBN)
Description
Using Large Corpora identifies new data-oriented methods for organizing and analyzing large corpora and describes the potential results that the use of large corpora offers.Today, large corpora consisting of hundreds of millions or even billions of words, along with new empirical and statistical methods for organizing and analyzing these data, promise new insights into the use of language. Already, the data extracted from these large corpora reveal that language use is more flexible and complex than most rule-based systems have tried to account for, providing a basis for progress in the performance of Natural Language Processing systems. Using Large Corpora identifies these new data-oriented methods and describes the potential results that the use of large corpora offers. The research described shows that the new methods may offer solutions to key issues of acquisition (automatically identifying and coding information), coverage (accounting for all of the phenomena in a given domain), robustness (accommodating "real data" that may be corrupt or not accounted for in the model), and extensibility (applying the model and data to a new domain, text, or problem). There are chapters on lexical issues, issues in syntax, and translation topics, as well discussions of the "statistics-based" vs. "rule-based" debate. ACL-MIT Series in Natural Language Processing.
More details
Series
Language
English
Place of publication
Cambridge, Mass.
United States
Publishing group
MIT Press Ltd
Target group
Adult education
College/higher education
Professional and scholarly
US School Grade: From College Freshman to College Graduate Student
Product notice
Paperback (trade)
Dimensions
Height: 249 mm
Width: 175 mm
Thickness: 23 mm
Weight
748 gr
ISBN-13
978-0-262-51082-0 (9780262510820)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification