9780367388638-0367388634-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: 9780367388638
ISBN-10: 0367388634
Edition: 1
Author: Steven Abney
Publication date: 2019
Publisher: Chapman and Hall/CRC
Format: Paperback 320 pages
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Book details

ISBN-13: 9780367388638
ISBN-10: 0367388634
Edition: 1
Author: Steven Abney
Publication date: 2019
Publisher: Chapman and Hall/CRC
Format: Paperback 320 pages

Summary

Semisupervised Learning for Computational Linguistics (Chapman & Hall/CRC Computer Science & Data Analysis) (ISBN-13: 9780367388638 and ISBN-10: 0367388634), written by authors Steven Abney, was published by Chapman and Hall/CRC in 2019. With an overall rating of 3.8 stars, it's a notable title among other books. You can easily purchase or rent Semisupervised Learning for Computational Linguistics (Chapman & Hall/CRC Computer Science & Data Analysis) (Paperback) from BooksRun, along with many other new and used books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.52.

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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