A Mixture Model for Learning Multi-Sense Word Embeddings
Table 4: Accuracies for the word analogy task. All our results are significantly higher than the result of Word2Vec Skip-gram (with two-tail p<0.001 using McNemar’s test). Pennington2014 used a larger training corpus of 1.6B words.
['Model', 'Accuracy (%)']
In particular, mswe reaches the accuracies of around 69.7% which is higher than the accuracy of 68.6% obtained by Word2Vec Skip-gram.