A Mixture Model for Learning Multi-Sense Word Embeddings
Table 2: Spearman’s rank correlation (ρ×100) for the word similarity task when using GlobalSim. Subscripts 50 and 200 denote the online LDA topic model trained with T=50 and T=200 topics, respectively. ⋆ denotes that our best score is significantly higher than the score of the baseline (with p< 0.05, online toolkit from http://www.philippsinger.info/?p=347). Scores in bold and underline are the best and second best scores.
['Model', 'rw', 'SimLex', 'scws', 'ws353', 'men']
We use subscripts 50 and 200 to denote the topic model trained with T=50 and T=200 topics, respectively. Specifically, on the rw dataset, mswe obtains a significant improvement of 2.92 in the Spearman’s rank correlation (which is about 8.5% relative improvement).