Size vs. structure in training corpora for word embedding models: Araneum Russicum Maximum and Russian National Corpus
Table 2: Correlations for different PoS subsets of RuSimLex965.
['[BOLD] PoS', '[BOLD] Araneum', '[BOLD] RNC', '[BOLD] Number of pairs']
If we measure the correlation on separate subsets consisting of pairs belonging to one and the same PoS, some interesting differences can be observed. For nouns and verbs, the RNC model is better, and for both models verbs are easier. Unexpected results show up for adjectives, which seem to be most difficult for the RNC models, but the easiest for the Araneum one: actually, this is the only PoS-subset on which the Araneum model outperforms the RNC-based one, for reasons unclear. Note also that the fact that the RNC model is better with nouns on RuSimLex965 does not prevent it from losing on the RuSSE sets which contain only nouns. This again proves that these sets feature different kind of similarity scores, arguably mixed with relatedness.