They assumed that words which have similar semantic meanings are likely to share similar sememes.
they propose to represent word meanings as embeddings (Pennington et al., 2014;Mikolov et al., 2013) learned from a large-scale text corpus, and they adopt collaborative filtering (Sarwar et al., 2001) and matrix factorization (Koren et al., 2009) for sememe prediction, which are concluded as Sememe Prediction with Word Embeddings (SPWE) and Sememe Prediction with Sememe Embeddings (SPSE) respectively.