Efficient Estimation of Word Representations in Vector Space
Table 3: Comparison of architectures using models trained on the same data, with 640-dimensional word vectors. The accuracies are reported on our Semantic-Syntactic Word Relationship test set, and on the syntactic relationship test set of [20]
['Model Architecture', 'Semantic-Syntactic Word Relationship test set Semantic Accuracy [%]', 'Semantic-Syntactic Word Relationship test set Syntactic Accuracy [%]', 'MSR Word Relatedness Test Set\xa0']
The NNLM vectors perform significantly better than the RNN - this is not surprising, as the word vectors in the RNNLM are directly connected to a non-linear hidden layer. The CBOW architecture works better than the NNLM on the syntactic tasks, and about the same on the semantic one. Finally, the Skip-gram architecture works slightly worse on the syntactic task than the CBOW model (but still better than the NNLM), and much better on the semantic part of the test than all the other models.