The paper describes SENSE, a word sense disambiguation system which makes use of
multidimensional analogy-based proportions to infer the most likely sense of a word given
its context. Architecture and functioning of the system are illustrated in detail. Results of
different experimental settings are given, showing that the system, in spite its conservative
bias, successfully copes with the problem of training data sparseness.