Effective Use of Word Order for Text Categorization with Convolutional Neural Networks To appear in NAACL HLT 2015.
Table 3: Error rate (%) comparison with previous best methods on IMDB.
['SVM bow2\xa0[WM12]', '10.84', '–']
Now we turn to parallel CNN. On IMDB, seq2-CNN, which has two seq-convolution layers (region size 2 and 3; 1000 neurons each; followed by one unit of max-pooling each), outperforms seq-CNN. We presume the effectiveness of seq2-CNN indicates that the length of predictive text regions is variable. Their result is not directly comparable with our supervised results due to use of additional resource. Nevertheless, our best result rivals their result.