this article. For the purpose of text classification, the answer keys serve only as a set of correct classifications for each text. If a text has instantiated key templates associated with it in the corpus, then it should be classified as a relevant text. If a text has no instantiated key templates associated with it (i.e., only a dummy template) then it should be classified as an irrelevant text. This is a binary classification problem: a text is either relevant to the terrorism domain or irrelevant. The texts were selected by keyword search from a database of newswire articles 2 because they contained words associated with terrorism. However, many of them did not mention any relevant terrorist incidents. Of the 1700 texts in the MUC4 corpus, only 53% described a relevant terrorist event. Because many of the texts in the corpus were irrelevant, the MUC-4 systems had to distinguish the relevant from the irrelevant texts. Although the MUC-4 task was information extraction, information detection 4 (i.e, text classification) was an implicit subtask. To be successful in MUC-4, the information extraction systems also had to be good at detection. Our MUC-4 system did not use a separate text classification module. Instead, we extracted information from every text and relied on a discourse analysis module to discard irrelevant templates. This strategy was very effective, 5 but it was expensive. A reliable text classification module could have filtered out irrele- 1MUC-3 was the Third Message Understanding ConferenCe held in 1991 [MUC-3 Proceedings 19911