This project evaluates two families of algorithms that can be used to automatically classify general texts within a set of conceptual categories. The first family uses indirect evidence in the form of term-category co-occurrence data. The second uses direct evidence based on the senses of the terms, where a term's senses are designated by the categories that it is a member of in a thesaurus. The direct evidence algorithms incorporate varying degrees of indirect evidence as well.

For these experiments a set of 3,864 conceptual categories were derived from the noun hierarchy of WordNet, an on-line thesaurus. The co-occurrence data for the associational and disambiguation algorithms was collected from a corpus of 3,711 AP newswire articles, comprising approximately 1.7 million words of text. Each of the algorithms was applied to all of the articles in the AP corpus, with their performance evaluated both qualitatively and quantitatively.

The results of these experiments show that both classes of algorithms have potential as fully automatic text classifiers. The direct methods produce qualitatively better classifications than the indirect ones when applied to AP newswire texts. The direct methods also achieve both a higher precision, 86.75% correctly classified (best case) versus 72.34%, and a higher approximate recall.

The experiments identify limiting factors on the performance of the algorithms. The primary limitations stem from the quality of the thesaural categories, which were derived automatically, and from the performance of the term sense disambiguation algorithm. The former can be addressed with human intervention, the latter with a larger training set for the statistical database.