Semisupervised Learning for Computational Linguistics

Steven Abney

ISBN 10: 0367388634 / ISBN 13: 9780367388638

Editore: Chapman And Hall/CRC, 2019

Riguardo questo articolo

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Codice articolo 9780367388638

Dati bibliografici

• Titolo: Semisupervised Learning for Computational Linguistics
• Editore: Chapman And Hall/CRC
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 0367388634
• ISBN 13: 9780367388638
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 234 L x 156 A x 18 P (millimetri) 234 millimetri di larghezza per 156 millimetri di altezza per 18 millimetri di profondità
• Peso: 494 grammi

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Abney, Steven