Current approaches for word sense disambiguation and translation selection typically require lexical resources or large bilingual corpora with rich information fields and annotations, which are often infeasible for under-resourced languages. We extract translation context knowledge from a bilingual comparable corpora of a richer-resourced language pair, and inject it into a multilingual lexicon. The multilingual lexicon can then be used to perform context-dependent lexical lookup on texts of any language, including under-resourced ones. Evaluations on a prototype lookup tool, trained on a English-Malay bilingual Wikipedia corpus, show a precision score of 0.65 (baseline 0.55) and mean reciprocal rank score of 0.81 (baseline 0.771). Based on the early encouraging results, the context-dependent lexical lookup tool may be developed further into an intelligent reading aid, to help users grasp the gist of a second or foreign language text
