Words that are homonyms-that is, for which a single written and spoken form is associated with multiple, unrelated interpretations, such as COMPOUND, which can denote an < enclosure > or a < composite > meaning-are an invaluable class of items for studying word and discourse comprehension. When using homonyms as stimuli, it is critical to control for the relative frequencies of each interpretation, because this variable can drastically alter the empirical effects of homonymy. Currently, the standard method for estimating these frequencies is based on the classification of free associates generated for a homonym, but this approach is both assumption-laden and resource-demanding. Here, we outline an alternative norming methodology based on explicit ratings of the relative meaning frequencies of dictionary definitions. To evaluate this method, we collected and analyzed data in a norming study involving 544 English homonyms, using the eDom norming software that we developed for this purpose. Dictionary definitions were generally sufficient to exhaustively cover word meanings, and the methods converged on stable norms with fewer data and less effort on the part of the experimenter. The predictive validity of the norms was demonstrated in analyses of lexical decision data from the English Lexicon Project (Balota et al., Behavior Research Methods, 39, 445-459, 2007), and from Armstrong and Plaut (Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, 2223-2228, 2011). On the basis of these results, our norming method obviates relying on the unsubstantiated assumptions involved in estimating relative meaning frequencies on the basis of classification of free associates. Additional details of the norming procedure, the meaning frequency norms, and the source code, standalone binaries, and user manual for the software are available at http://edom.cnbc.cmu.edu.
