We describe an approach to text classification that represents a compromise between traditional word-based techniques and in-depth natural language processing. Our approach uses a natural language processing task called ''information extraction'' as a basis for high-precision text classification. We present three algorithms that use varying amounts of extracted information to classify texts. The relevancy signatures algorithm uses linguistic phrases; the augmented relevancy signatures algorithm uses phrases and local context; and the case-based text classification algorithm uses larger pieces of context. Relevant phrases and contexts are acquired automatically using a training corpus. We evaluate the algorithms on the basis of two test sets from the MUC-4 corpus. All three algorithms achieved high precision on both test sets, with the augmented relevancy signatures algorithm and the case-based algorithm reaching 100% precision with over 60% recall on one set. Additionally, we compare the algorithms on a larger collection of 1700 texts and describe an automated method for empirically deriving appropriate threshold values. The results suggest that information extraction techniques can support high-precision text classification and, in general, that using more extracted information improves performance. As a practical matter, we also explain how the text classification system can be easily ported across domains.
