《Machine Learning in Automated Text Categorization》.pdf

Machine Learning in Automated Text Categorization FABRIZIO SEBASTIANI Consiglio Nazionale delle Ricerche, Italy 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 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. Categories and Subject Descriptors: H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing—Indexing methods ; H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—Information filtering ; H.3.4 [Information Storage and Retrieval]: Systems and Software—Performance evaluation (efficiency and effectiveness); I.2.6 [Artificial Intelligence]: Learning— Induction General Terms: Algorithms, Experimentation, Theory Additional Key Words and Phrases: Machine learning, text categorization, text