The sentences in all datasets were tokenized, compound-split, and lemmatized, and for each target word we automatically determined the set of possible senses, given its context and inflec-  Table 3: WSD accuracy on baselines, UKB, and the three variants of our model (ρ = 0.9) on all test sets.
if by using the SN we intend to learn clear separations between different senses of a word, it attends to reason to limit its application to those cases, while monosemous words can be sufficiently well trained by the usual corpus-based approach, and act as semantic anchors in the broader vector space.