Machine Learning in Automated Text Categorization

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Machine Learning in Automated Text Categorization (2002)

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by Fabrizio Sebastiani

Venue:	ACM COMPUTING SURVEYS
Citations:	1734 - 22 self

BibTeX

@ARTICLE{Sebastiani02machinelearning,
    author = {Fabrizio Sebastiani},
    title = {Machine Learning in Automated Text Categorization},
    journal = {ACM COMPUTING SURVEYS},
    year = {2002},
    volume = {34},
    pages = {1--47}
}

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Abstract

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten 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 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.

Keyphrases

automated text categorization machine learning good effectiveness knowledge engineering approach general inductive process expert labor power predefined category document representation digital form dominant approach booming interest main approach automated categorization manual definition different domain detail issue considerable saving different problem domain expert last ten year research community preclassified document

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