https://www.crcpress.com/Semisupervised-Learning-for-Computational-Linguistics/Abney/9781584885597?source=igodigital

FeaturesOffers applications in information extraction, parsing, and word senses, such as WordNetProvides background material in machine learning that includes the areas of classification and clusteringCovers a variety of methods, including co-boosting, transductive SVMs, McLachlan's algorithm, and the EM algorithmExamines in detail the concept of label propagation in a graphDiscusses spectral methods, including the definition of harmonics, the eigenvectors of matrices and graphs, spectral clustering, and the connection to label propagationIntroduces the necessary mathematics in a just-in-time manner

Summary
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.