Machine learning in automated text categorization

Abstract

The automated categorization of text into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of pre-classified documents, the characteristics of the categories. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. Issues pertaining to three different problems are discussed in detail, namely, document representation, classifier construction and classifier evaluation.