Multi-Word Expressions (MWEs) are prevalent in text and are also, on average, less polysemous than mono-words. This suggests that accurate MWE detection should lead to a nontrivial improvement in Word Sense Disambiguation (WSD). We show that a straightforward MWE detection strategy, due to Arranz et al. (2005), can increase a WSD algorithm's baseline f-measure by 5 percentage points. Our measurements are consistent with Arranz's, and our study goes further by using a portion of the Semcor corpus containing 12,449 MWEs - over 30 times more than the approximately 400 used by Arranz. We also show that perfect MWE detection over Semcor only nets a total 6 percentage point increase in WSD f-measure; therefore there is little room for improvement over the results presented here. We provide our MWE detection algorithms, along with a general detection framework, in a free, open-source Java library called jMWE.
