Resnik and Yarowsky (1997) made a set of observations about the state-of-the-art in automatic word sense disambiguation and, motivated by those observations, offered several specific proposals regarding improved evaluation criteria, common training and testing resources, and the definition of sense inventories. Subsequent discussion of those proposals resulted in SENSEVAL, the first evaluation exercise for word sense disambiguation (Kilgarriff and Palmer 2000). This article is a revised and extended version of our 1997 workshop paper, reviewing its observations and proposals and discussing them in light of the SENSEVAL exercise. It also includes a new in-depth empirical study of translingually-based sense inventories and distance measures, using statistics collected from native-speaker annotations of 222 polysemous contexts across 12 languages. These data show that monolingual sense distinctions at most levels of granularity can be effectively captured by translations into some set of second languages, especially as language family distance increases. In addition, the probability that a given sense pair will tend to lexicalize differently across languages is shown to correlate with semantic salience and sense granularity; sense hierarchies automatically generated from such distance matrices yield results remarkably similar to those created by professional monolingual lexicographers.