Several techniques have been proposed that either extend word embedding models to cluster contexts and induce senses, usually referred to as unsupervised sense representations (Schütze, 1998;Reisinger and Mooney, 2010;Huang et al., 2012;Neelakantan et al., 2014;Guo et al., 2014;Tian et al., 2014;Šuster et al., 2016;Ettinger et al., 2016;Qiu et al., 2016) or exploit external sense inventories and lexical resources for generating sense representations for individual meanings of words Johansson and Pina, 2015;Jauhar et al., 2015;Iacobacci et al., 2015;Rothe and Schütze, 2015;Camacho-Collados et al., 2016b;Mancini et al., 2016;Pilehvar and Collier, 2016).
the integration of sense representations into deep learning models has not been so straightforward, and research in this field has often opted for alternative evaluation benchmarks such as WSD, or artificial tasks, such as word similarity.