1 Introduction
Table 1: Test set performance of the ML models for 20-class document classification.
['ML Model', 'Test Accuracy (%)']
Herein CNN1, CNN2, CNN3 respectively denote neural networks with convolutional filter size H equal to 1, 2 and 3 (i.e. covering 1, 2 or 3 consecutive words in the document). One can see that the linear SVM performs on par with the neural networks, i.e. the non-linear structure of the CNN models does not yield a considerable advantage toward classification accuracy. This can be explained by the fact that for most topic categorization tasks, the different categories can be separated linearly in the very high-dimensional bag-of-words or bag-of-N-grams space thanks to sufficiently disjoint sets of features.