Previous work addressing the problem can be roughly classified into three categories: (1) learning word embeddings from large collections of text using variants of neural networks (Mikolov et al. (2013a) ; Mikolov et al. (2013b) ; Mikolov et al. (2013c) ;<cite> Levy and Goldberg (2014)</cite> ) or global matrix factorization (Deerwester et al. (1990) ; Turney (2012) ); (2) extracting knowledge from existing semantic networks, such as WordNet (Yang and Powers (2005) ; Alvarez and Lim (2007) ; Hughes and Ramage (2007) ) and ConceptNet (Boteanu and Chernova (2015) ); (3) combining the above two models by various ways (Agirre et al. (2009) ; Zhila et al. (2013) ; Iacobacci et al. (2015) ; Summers-Stay et al. (2016) ).