Convolutional Neural Networks for Text Categorization: Shallow Word level vs. Deep Character level
This paper reports the performances of shallow word level convolutional neural networks (CNN) our earlier work (2015) on the eight datasets with relatively large training data that were used for testing the very deep character level CNN in Conneau et al. (2016) Our findings are as follows. The shallow word level CNNs achieve better error rates than the error rates reported in Conneau et al. though the results should be interpreted with some consideration due to the unique pre processing of Conneau et al. The shallow word level CNN uses more parameters and therefore requires more storage than the deep character level CNN; however, the shallow word level CNN computes much faster.
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