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Most words in natural languages are polysemous in nature that is they have multiple possible meanings or senses. The sense in which the word is used determines the translation of the word. We show that incorporating a sense-based translation model into statistical machine translation model consistently improves translation quality across all different test sets of five different language-pairs, according to all eight most commonly used evaluation metrics. This paper is an investigation on how to initiate research in word sense disambiguation and statistical machine translation for under-resourced languages by applying Word Sense Induction.

Word Sense Induction for Better Lexical Choice