A Short Survey on Sense-Annotated Corpora for Diverse Languages and Resources

With the advancement of research in word sense disambiguation and deep learning, large sense-annotated datasets are increasingly important for training supervised systems. However, gathering high-quality sense-annotated data for as many instances as possible is an arduous task. This has led to the proliferation of automatic and semi-automatic methods for overcoming the so-called knowledge-acquisition bottleneck. In this paper we present an overview of currently available sense-annotated corpora, both manually and automatically constructed, for various languages and resources (i.e. WordNet, Wikipedia, BabelNet). General statistics and specific features of each sense-annotated dataset are also provided.