Probabilistic FastText for Multi-Sense Word Embeddings
Table 2: Spearman’s Correlation ρ×100 on Word Similarity Datasets.
['d', '50 w2g', '50 w2gm', '50 pft-g', '50 pft-gm', '300 FastText', '300 w2g', '300 w2gm', '300 pft-g', '300 pft-gm']
4.3.1 Comparison Against Dictionary-Level Density Embeddings and FastText Our pft-gm achieves the highest average score among all competing models, outperforming both FastText and the dictionary-level embeddings w2g and w2gm. Our unimodal model pft-g also outperforms the dictionary-level counterpart w2g and FastText. However, the datasets that w2gm performs better than pft-gm often have small sizes such as MC-30 or RG-65, where the Spearman’s correlations are more subject to noise. Overall, pft-gm outperforms w2gm by 3.1% and 8.7% in 300 and 50 dimensional models. In addition, pft-g and pft-gm also outperform FastText by 1.2% and 3.7% respectively.