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.
KeywordsSupport Vector Machine Feature Selection Class Label Feature Selection Method Text Classification
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