Offers applications in information extraction, parsing, and word senses, such as WordNet

Provides background material in machine learning that includes the areas of classification and clustering

Covers a variety of methods, including co-boosting, transductive SVMs, McLachlan's algorithm, and the EM algorithm

Examines in detail the concept of label propagation in a graph

Discusses spectral methods, including the definition of harmonics, the eigenvectors of matrices and graphs, spectral clustering, and the connection to label propagation

Introduces the necessary mathematics in a just-in-time manner

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.

INTRODUCTION
A brief history
Semisupervised learning
Organization and assumptions

SELF-TRAINING AND CO-TRAINING
Classification
Self-training
Co-training

APPLICATIONS OF SELF-TRAINING AND CO-TRAINING
Part-of-speech tagging
Information extraction
Parsing
Word senses

CLASSIFICATION
Two simple classifiers
Abstract setting
Evaluating detectors and classifiers that abstain
Binary classifiers and ECOC

MATHEMATICS FOR BOUNDARY-ORIENTED METHODS
Linear separators
The gradient
Constrained optimization

BOUNDARY-ORIENTED METHODS
The perceptron
Game self-teaching
Boosting
Support vector machines (SVMs)
Null-category noise model

CLUSTERING
Cluster and label
Clustering concepts
Hierarchical clustering
Self-training revisited
Graph mincut
Label propagation
Bibliographic notes

GENERATIVE MODELS
Gaussian mixtures
The EM algorithm

AGREEMENT CONSTRAINTS
Co-training
Agreement-based self-teaching
Random fields
Bibliographic notes

PROPAGATION METHODS
Label propagation
Random walks
Harmonic functions
Fluids
Computing the solution
Graph mincuts revisited
Bibliographic notes

MATHEMATICS FOR SPECTRAL METHODS
Some basic concepts
Eigenvalues and eigenvectors
Eigenvalues and the scaling effects of a matrix
Bibliographic notes

SPECTRAL METHODS
Simple harmonic motion
Spectra of matrices and graphs
Spectral clustering
Spectral methods for semisupervised learning
Bibliographic notes

BIBLIOGRAPHY
INDEX

Editorial Reviews

"…I would have loved to have had this book when I started working as a computational linguist … The book is well laid out, enjoyable to read, and the formulae aesthetically presented … The book does a very amicable job of being self-contained given the number of subjects and size of the book. I would recommend this book to mathematicians, statisticians, and libraries alike."
—CHOICE, February 2009

"However when it works, it works well, and whereas the book provides great breadth, but little depth, it will be a useful springboard for the beginning student."

– Chris J.C. Burges, Microsoft Research, in Journal of the American Statistical Association, June 2009, Vol. 104, No. 486