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This paper describes a bootstrapping algorithm called Basilisk that learns high-quality semantic lexicons for multiple categories. Basilisk begins with an unannotated corpus and seed words for each semantic category, which are then bootstrapped to learn new words for each category. Basilisk hypothesizes the semantic class of a word based on collective information over a large body of extraction pattern contexts. We evaluate Basilisk on six semantic categories. The semantic lexicons produced by Basilisk have higher precision than those produced by previous techniques, with several categories showing substantial improvement.