  Disambiguation of word senses in context is easy for humans, but is a major
challenge for automatic approaches. Sophisticated supervised and
knowledge-based models were developed to solve this task. However, (i) the
inherent Zipfian distribution of supervised training instances for a given word
and/or (ii) the quality of linguistic knowledge representations motivate the
development of completely unsupervised and knowledge-free approaches to word
sense disambiguation (WSD). They are particularly useful for under-resourced
languages which do not have any resources for building either supervised and/or
knowledge-based models. In this paper, we present a method that takes as input
a standard pre-trained word embedding model and induces a fully-fledged word
sense inventory, which can be used for disambiguation in context. We use this
method to induce a collection of sense inventories for 158 languages on the
basis of the original pre-trained fastText word embeddings by Grave et al.
(2018), enabling WSD in these languages. Models and system are available
online.
