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.
Original language | English (US) |
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Pages (from-to) | 307-316 |
Number of pages | 10 |
Journal | SIGIR Forum (ACM Special Interest Group on Information Retrieval) |
State | Published - Dec 1 1996 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Management Information Systems
- Hardware and Architecture