Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database
Table 1: Skip-gram’s parameter selection. Silhouette scores for the categorization test and correlations for the WordSim353 test. In all cases the embedding dimensions were set to 100.
['Silhouette score', 'TASA', '5', 'win \\neg 0.107', '5 0.107', '10 0.109', '15']
The best scores where selected to perform the corpus size analysis. In the semantic categorization test, in the case of TASA corpus, neg = 15 was chosen given that it showed slightly better performance.