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Sebastiani, F.: Classification of text, automatic (2006) 0.02

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Abstract: Automatic text classification (ATC) is a discipline at the crossroads of information retrieval (IR), machine learning (ML), and computational linguistics (CL), and consists in the realization of text classifiers, i.e. software systems capable of assigning texts to one or more categories, or classes, from a predefined set. Applications range from the automated indexing of scientific articles, to e-mail routing, spam filtering, authorship attribution, and automated survey coding. This article will focus on the ML approach to ATC, whereby a software system (called the learner) automatically builds a classifier for the categories of interest by generalizing from a "training" set of pre-classified texts.

Sebastiani, F.: Machine learning in automated text categorization (2002) 0.02

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Abstract: The automated categorization (or classification) of texts 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 an machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.