Molding CNNs for text: non-linear, non-consecutive convolutions
Table 3: Performance of various methods on Chinese news categorization task. Our model obtains better results than the SVM, NBoW and traditional CNN baselines.
['[BOLD] Model', '[BOLD] Dev Acc.', '[BOLD] Test Acc.']
Since the dataset is much larger compared to the sentiment dataset (80K documents vs. 8.5K sentences), the SVM method is a competitive baseline. It achieves 78.5% accuracy compared to 74.4% and 79.2% obtained by the neural bag-of-words model and CNN model. In contrast, our model obtains 80.0% accuracy on both the development and test sets, outperforming the three baselines by a 0.8% absolute margin. The best hyperparameter configuration in this task uses less feature layers and lower n-gram order (specifically, 2 layers and n=2) compared to the sentiment classification task. We hypothesize that the difference is due to the nature of the two tasks: the document classification task requires to handle less compositions or context interactions than sentiment analysis.