{
"item_title" : "Semisupervised Learning for Computational Linguistics",
"item_author" : [" Steven Abney "],
"item_description" : "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.",
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Overview
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.
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Details
- ISBN-13: 9781584885597
- ISBN-10: 1584885599
- Publisher: CRC Press
- Publish Date: September 2007
- Dimensions: 9.58 x 6.38 x 0.88 inches
- Shipping Weight: 1.32 pounds
- Page Count: 322
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