ABSTRACT

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

chapter 1|12 pages

Introduction

chapter 2|18 pages

Self-training and Co-training

chapter 4|24 pages

Classification

chapter 5|28 pages

Mathematics for Boundary-Oriented Methods

chapter 6|36 pages

Boundary-Oriented Methods

chapter 7|22 pages

Clustering

chapter 8|22 pages

Generative Models

chapter 9|18 pages

Agreement Constraints

chapter 10|28 pages

Propagation Methods

chapter 11|16 pages

Mathematics for Spectral Methods

chapter 12|40 pages

Spectral Methods