Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation?
Table 4: Comparison of performances (Spearman’s ρ) between GloVe combined with Word2vec (W2V) against GloVe combined with DT embedding obtained using node2vec (D2V-N). PCA has been taken as combination method. Clearly, DT embedding outperforms Word2vec in terms of enhancing the performance of GloVe.
['[BOLD] Dataset', '[BOLD] PCA (GloVe,W2V)', '[BOLD] PCA (GloVe,D2V-N)']
Further, to scrutinize that the achieved result is not just the effect of combining two different word vectors, we compare PCA (GloVe, D2V-N) against combination of GloVe and Word2vec (W2V).