Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation?
Table 5: Performance (ρ) reported for three combined representations: GloVe and DT embedding using node2vec (D2V-N), GloVe and WordNet embedding (WN2V), GloVe, WN2V and D2V-N. Results show that, DT embedding produces comparable performance w.r.t WordNet embedding. Combining DT embedding along with WordNet embedding helps to boost performance in further many of the cases.
['[BOLD] Dataset', '[BOLD] PCA (GloVe, D2V-N)', '[BOLD] PCA (GloVe, WN2V)', '[BOLD] PCA (GloVe, WN2V, D2V-N)']
Now, as we observe that the network embedding from DT network helps to boost the performance of Word2vec and GloVe when combined with them, we further compare the performance against the case when text based embeddings are combined with embeddings from lexical resources. For that purpose, we take one baseline Goikoetxea et al. Here we use GloVe as the text based representation and PCA as the combination method as prescribed by the author. Note that, WordNet based representation is made publicly available by Goikoetxea et al. Additionally we check whether we gain in terms of performance if we integrate the three embeddings together. Looking at the performance, we can conclude that automatically generated DT network from corpus brings in useful additional information as far as word similarity and relatedness tasks are concerned.