9781584885597-1584885599-Semisupervised Learning for Computational Linguistics (Chapman & Hall/CRC Computer Science & Data Analysis)

Semisupervised Learning for Computational Linguistics (Chapman & Hall/CRC Computer Science & Data Analysis)

ISBN-13: 9781584885597
ISBN-10: 1584885599
Edition: 1
Author: Abney, Steven
Publication date: 2007
Publisher: Chapman and Hall/CRC
Format: Hardcover 324 pages
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Book details

ISBN-13: 9781584885597
ISBN-10: 1584885599
Edition: 1
Author: Abney, Steven
Publication date: 2007
Publisher: Chapman and Hall/CRC
Format: Hardcover 324 pages

Summary

Acknowledged authors Abney, Steven wrote Semisupervised Learning for Computational Linguistics (Chapman & Hall/CRC Computer Science & Data Analysis) comprising 324 pages back in 2007. Textbook and eTextbook are published under ISBN 1584885599 and 9781584885597. Since then Semisupervised Learning for Computational Linguistics (Chapman & Hall/CRC Computer Science & Data Analysis) textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

The rapid advancement in the theoretical understanding of statistical and machine learning methods for semisupervised learning has made it difficult for nonspecialists to keep up to date in the field. Providing a broad, accessible treatment of the theory as well as linguistic applications, Semisupervised Learning for Computational Linguistics offers self-contained coverage of semisupervised methods that includes background material on supervised and unsupervised learning.

The book presents a brief history of semisupervised learning and its place in the spectrum of learning methods before moving on to discuss well-known natural language processing methods, such as self-training and co-training. It then centers on machine learning techniques, including the boundary-oriented methods of perceptrons, boosting, support vector machines (SVMs), and the null-category noise model. In addition, the book covers clustering, the expectation-maximization (EM) algorithm, related generative methods, and agreement methods. It concludes with the graph-based method of label propagation as well as a detailed discussion of spectral methods.

Taking an intuitive approach to the material, this lucid book facilitates the application of semisupervised learning methods to natural language processing and provides the framework and motivation for a more systematic study of machine learning.

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