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 offer
 

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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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