(2016) conducted a series of experiments to study the effect that the variation of sense distributions in the training set has on the performances of It makes sense (Zhong and Ng, 2010).
they increased the volume of training examples (V) by enriching SemCor with senses inferred from BabelNet; increased the number of least frequent senses (LFS) (V+LFS); and overfitted the model constructing a training set proportional to the correct sense distribution of the test set (GOLD,GOLD+LFS).