Text Categorization and Relational Learning
نویسنده
چکیده
We evaluate the rst order learning system FOIL on a series of text categorization problems. It is shown that FOIL usually forms classiiers with lower error rates and higher rates of precision and recall with a relational encoding than with a propositional encoding. We show that FOIL's performance can be improved by relation selection, a rst order analog of feature selection. Relation selection improves FOIL's performance as measured by any of recall, precision, F-measure, or error rate. With an appropriate level of relation selection, FOIL appears to be competitive with or superior to existing proposi-tional techniques.
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