  Large sense-annotated datasets are increasingly necessary for training deep
supervised systems in Word Sense Disambiguation. However, gathering
high-quality sense-annotated data for as many instances as possible is a
laborious and expensive task. This has led to the proliferation of automatic
and semi-automatic methods for overcoming the so-called knowledge-acquisition
bottleneck. In this short survey we present an overview of sense-annotated
corpora, annotated either manually- or (semi)automatically, that are currently
available for different languages and featuring distinct lexical resources as
inventory of senses, i.e. WordNet, Wikipedia, BabelNet. Furthermore, we provide
the reader with general statistics of each dataset and an analysis of their
specific features.
