  The paper describes the results of the first shared task on word sense
induction (WSI) for the Russian language. While similar shared tasks were
conducted in the past for some Romance and Germanic languages, we explore the
performance of sense induction and disambiguation methods for a Slavic language
that shares many features with other Slavic languages, such as rich morphology
and virtually free word order. The participants were asked to group contexts of
a given word in accordance with its senses that were not provided beforehand.
For instance, given a word "bank" and a set of contexts for this word, e.g.
"bank is a financial institution that accepts deposits" and "river bank is a
slope beside a body of water", a participant was asked to cluster such contexts
in the unknown in advance number of clusters corresponding to, in this case,
the "company" and the "area" senses of the word "bank". For the purpose of this
evaluation campaign, we developed three new evaluation datasets based on sense
inventories that have different sense granularity. The contexts in these
datasets were sampled from texts of Wikipedia, the academic corpus of Russian,
and an explanatory dictionary of Russian. Overall, 18 teams participated in the
competition submitting 383 models. Multiple teams managed to substantially
outperform competitive state-of-the-art baselines from the previous years based
on sense embeddings.
