Efficient Estimation of Word Representations in Vector Space
Table 7: Comparison and combination of models on the Microsoft Sentence Completion Challenge.
['Architecture', 'Accuracy [%]']
While the Skip-gram model itself does not perform on this task better than LSA similarity, the scores from this model are complementary to scores obtained with RNNLMs, and a weighted combination leads to a new state of the art result 58.9% accuracy (59.2% on the development part of the set and 58.7% on the test part of the set).