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We present a decision-tree-based symbolic rule induction system for categorizing text documents automatically. Our method for rule induction involves the novel combination of (1) a fast decision tree induction algorithm especially suited to text data and (2) a new method for converting a decision tree to a rule set that is simplified, but still logically equivalent to, the original tree. We report experimental results on the use of this system on some practical problems.
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