Semisupervised Learning for Computational Linguistics

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CRC Press, Sep 17, 2007 - Business & Economics - 320 pages
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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Contents

Introduction
1
Selftraining and Cotraining
13
Applications of SelfTraining and CoTraining
31
Classification
43
Mathematics for BoundaryOriented Methods
67
BoundaryOriented Methods
95
Clustering
131
Generative Models
153
Agreement Constraints
175
Propagation Methods
193
Mathematics for Spectral Methods
221
Spectral Methods
237
Bibliography
277
Index
301
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