Context-sensitive learning methods for text categorization

作者：William W. Cohen, Yoram Singer · 发表于：ACM Transactions on Information Systems · 年份：1999 · DOI：10.1145/306686.306688 · 被引用次数：358 · 研究领域：Text and Document Classification Technologies、Machine Learning and Algorithms、Algorithms and Data Compression

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.