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 selfcontained 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 wellknown natural language processing methods such as selftraining and cotraining. It then centers on machine learning techniques including the boundaryoriented methods of perceptrons boosting support vector machines SVMs and the nullcategory noise model. In addition the book covers clustering the expectationmaximization EM algorithm related generative methods and agreement methods. It concludes with the graphbased 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.