Year: 1999
Type: article
Abstract: Two recently implemented machine-learning algorithms, RIPPER and sleeping-experts for phrases , are evaluated on a number of large text categorization problems. These algorithms both construct classifiers that allow the “context” of a word w to affect how (or even whether) the presence or absence of w will contribute to a classification. However, RIPPER and sleeping-experts differ radically in many other respects: differences include different notions as to what constitutes a context, different ways of combining contexts to construct a classifier, different methods to search for a combination of contexts, and different criteria as to what contexts should be included in such a combination. In spite of these differences, both RIPPER and sleeping-experts perform extremely well across a wide variety of categorization problems, generally outperforming previously applied learning methods. We view this result as a confirmation of the usefulness of classifiers that represent contextual information.
Authors: William W. Cohen, Yoram Singer
Institution: AT&T (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W1969572066