  In the paper, we test two different approaches to the {unsupervised} word
sense disambiguation task for Polish. In both methods, we use neural language
models to predict words similar to those being disambiguated and, on the basis
of these words, we predict the partition of word senses in different ways. In
the first method, we cluster selected similar words, while in the second, we
cluster vectors representing their subsets. The evaluation was carried out on
texts annotated with plWordNet senses and provided a relatively good result
(F1=0.68 for all ambiguous words). The results are significantly better than
those obtained for the neural model-based unsupervised method proposed in
\cite{waw:myk:17:Sense} and are at the level of the supervised method presented
there. The proposed method may be a way of solving word sense disambiguation
problem for languages that lack sense annotated data.
