Semisupervised Learning for Computational Linguistics

Steven Abney

ISBN 10: 1584885599 / ISBN 13: 9781584885597

Published by Taylor & Francis Ltd Sep 2007, 2007

About this Item

Neuware - This book provides a broad, accessible treatment of the theory and linguistic applications of semisupervised methods. It presents a brief history of the field 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, SVMs, and the null-category noise model. In addition, the book covers clustering, the 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.

Seller Inventory # 9781584885597

Bibliographic Details

• Title: Semisupervised Learning for Computational Linguistics
• Publisher: Taylor & Francis Ltd Sep 2007
• Publication Date: 2007
• Language: English
• ISBN 10: 1584885599
• ISBN 13: 9781584885597
• Binding: Buch
• Condition: Neu
• Dimensions: 240W x 161H x 22D millimeters 240 millimeters width by 161 millimeters height by 22 millimeters depth
• Weight: 652 grams

About this title

Synopsis

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.

About the Author

Abney, Steven