Semisupervised Learning for Computational Linguistics

Semisupervised Learning for Computational Linguistics

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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 more

Product details

  • Hardback | 320 pages
  • 152.4 x 233.68 x 22.86mm | 589.67g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 97 black & white illustrations
  • 1584885599
  • 9781584885597
  • 2,449,255

About Steven Abney

University of Michigan, Ann Arbor, USAshow more

Table of contents

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 INDEXshow more

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