This chapter reviews the state-of-the-art in learning text classifiers from examples. First, it gives formal definitions of the most common scenarios in text classification — namely binary, multi-class, and multi-label classification. Furthermore, it gives an overview of different representations of text, feature selection methods, and criteria for evaluating predictive performance. The chapter ends with a description of the experimental setup used throughout this book.


Support Vector Machine Feature Selection Class Label Feature Selection Method Text Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Thorsten Joachims
    • 1
  1. 1.Cornell UniversityUSA

Personalised recommendations