Card-660: Cambridge Rare Word Dataset – a Reliable Benchmark for Infrequent Word Representation Models
Table 4: Pearson r and Spearman ρ correlation percentage performance of mainstream pre-trained word embeddings on the RW and Card-660 datasets. Column |V| shows the size of vocabulary for the corresponding embedding set.
['[BOLD] Embedding set', '| [ITALIC] V|', '[BOLD] Missed words RW', '[BOLD] Missed words Card', '[BOLD] Missed pairs RW', '[BOLD] Missed pairs Card', '[BOLD] Pearson  [ITALIC] r RW', '[BOLD] Pearson  [ITALIC] r Card', '[BOLD] Spearman  [ITALIC] ρ RW', '[BOLD] Spearman  [ITALIC] ρ Card']
Pennington et al. Speer et al. In the last two rows of the Table we also report results for two hybrid embeddings constructed by combining the pre-trained Freebase Word2vec, which mostly comprises named entities, with two of the best performing embeddings evaluated on the dataset. Given that the word embeddings are not comparable across two different spaces, we only compute the similarity between a pair only if both words are covered in the same space (with priority given to the non-Freebase embedding).