This paper examines extending a database of English verbs, grouped into syntactico-semantic classes, with WordNet senses. Probabilistic associations between theta-grids and WordNet verb frames, SEMCOR frequency data, and disambiguation based on an information-theoretic notion of semantic similarity are used. Mapping successes and failures are illustrated with 'drop'. (Cross-referenced as UMIACS-TR-2001-18) (Cross-refereneced as LAMP-TR-068)