Alleviating Overfitting for Polysemous Words for Word Representation Estimation Using Lexicons
TABLE IV: Spearman’s rank correlations ρ with Simlex-999 [39] when paraphrases of different types were used. “Entailment” set in every test includes “forward entailment” and “reverse entailment.”
['Paraphrase Set', '[ITALIC] ρ']
We also compare the cosine similarities of words with those in the gold standard dataset Simlex-999 Simlex-999 contains 999 word pairs. Each pair is annotated with a semantic similarity by humans. The annotators were told to be careful about the difference of “semantically similarity” and “relatedness.” They give high scores to “semantical similar words” like “coast” and “shore” but give low scores to “related but not similar words” like “clothes” and “closet.” We use it to see how the word vectors capture the meaning of words. We report the Spearman’s rank correlations ρ. Simlex-999. Using the other types deteriorates the scores.