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Context-sensitive learning methods for text categorization (1998).
| Content Provider | CiteSeerX |
|---|---|
| Author | Cohen, William W. Singer, Yoram |
| Abstract | Two recently implemented machine learning algorithms, RIPPER and sleepingexperts 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 informati... |
| File Format | |
| Publisher Date | 1998-01-01 |
| Access Restriction | Open |
| Subject Keyword | Text Categorization Context-sensitive Learning Method Categorization Problem Many Respect Different Notion Different Method Different Criterion Wide Variety Construct Classifier Different Way Sleeping-experts Perform Contextual Informati Large Text Categorization Problem |
| Content Type | Text |
