Machine Learning in Automated Text Categorisation

The automated categorisation (or classification) of texts into topical categories has a long history, dating back at least to the early ''60s. Until the late ''80s, the most effective approach to the problem seemed to be that of manually building automatic classifiers by means of {\em knowledge-engineering} techniques, i.e.\ manually defining a set of rules encoding expert knowledge on how to classify documents under a given set of categories. In the ''90s, with the booming production and availability of on-line documents, automated text categorisation has witnessed an increased and renewed interest, prompted by which the {\em machine learning} paradigm to automatic classifier construction has emerged and definitely superseded the knowledge-engineering approach. Within the machine learning paradigm, a general inductive process (called the {\em learner}) automatically builds a classifier (also called the {\em rule}, or the {\em hypothesis}) by ``learning'''', from a set of previously classified documents, the characteristics of one or more categories. The advantages of this approach are a very good effectiveness, a considerable savings in terms of expert manpower, and domain independence. In this survey we look at the main approaches that have been taken towards automatic text categorisation within the general machine learning paradigm. Issues pertaining to document indexing, classifier construction, and classifier evaluation, will be discussed in detail. A final section will be devoted to the techniques that have specifically been devised for an emerging application such as the automatic classification of Web pages into ``{\sc Yahoo!}-like'''' hierarchically structured sets of categories.