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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