Unsupervised Document Embedding With CNNs
Table 4: Amazon results for two and five class sentiment classification tasks.
['Method', '2-class', '5-class']
From the table, we see that CNN-pool-k performs comparably to the best baselines on both classification tasks. Max pooling again produces better performance than padding leading to conclusion that pooling should be used as the default method to deal with variable length input. Strong performance on the 5-class classification task suggest that our model is capable of successfully learning fine-grained differences between sentiment directly from unlabeled text. Overall, together with IMDB these results further indicate that the convolutional architecture is well suited for unsupervised NLP, and can be used to learn robust embedding models.