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

Introduction. Self-Training and Co-Training. Applications of Self-Training and Co-Training. Classification. Mathematics for Boundary-Oriented Methods. Boundary-Oriented Methods. Clustering. Generative Models. Agreement Constraints. Propagation Methods. Mathematics for Spectral Methods. Spectral Methods. Bibliography.