Table 4 provides correlation performance results for different embedding sets on the RW and CARD-660 datasets.
we considered different variants of Word2vec 7 (Mikolov et al.,7 https://code.google.com/archive/p/word2vec/ 2013) and Glove 8 (Pennington et al., 2014), two commonly-used word embeddings that are trained on massively large text corpora; Dependencybased embeddings 9 (Levy and Goldberg, 2014) which extends the Skip-gram model to handle dependency-based contexts; LexVec 10 (Salle et al., 2016) which improves the Skip-gram model to better handle frequent words; and ConceptNet Numberbatch 11 (Speer et al., 2017) which exploits lexical knowledge from multiple resources, such as Wiktionary and WordNet, and was the best performing system in SemEval 2017 Task 2.