A Mixture Model for Learning Multi-Sense Word Embeddings
Table 1: The benchmark datasets. ws353: WordSimilarity-353. rw: Rare-Words. SimLex: SimLex-999. scws: Stanford’s Contextual Word Similarities. men: The MEN Test Collection. Each dataset contains similarity scores of human judgments for pairs of words.
['Dataset', 'Word pairs', 'Reference']
For a given dataset of word pairs, the evaluation is done by calculating correlation between the similarity scores of corresponding word embedding pairs with the human judgment scores. Higher Spearman’s rank correlation (ρ) reflects better word embedding model.