Word Sense Disambiguation (WSD) is an intermediate task that serves as a means to an end defined by the application in which it is to be used. However, different applications have varying disambiguation needs which should have an impact on the choice of the method and of the sense inventory used. The tendency towards application-oriented WSD becomes more and more evident, mostly because of the inadequacy of predefined sense inventories and the inefficacy of application-independent methods in accomplishing specific tasks. In this article, we present a data-driven method of sense induction, which combines contextual and translation information coming from a bilingual parallel training corpus. It consists of an unsupervised method that clusters semantically similar translation equivalents of source language (SL) polysemous words. The created clusters are projected on the SL words revealing their sense distinctions. Clustered equivalents describing a sense of a polysemous word can be cons...