Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction
Table 5: The effect of the length cutoff policy on the number of words distribution.
['[EMPTY]', 'average words per example', 'median words per example']
However, rather than limiting the number of sentences, we instead opted for a cutoff on the maximum allowed number of characters, which we set to 20000. We found that with hierarchical attention networks the latter gives better results than using the 360 sentences cutoff, even though on average it corresponds to less words. We found that fixing the number of sentences instead of the number of characters leads to a large variance in the number of words of examples. A likely cause for this is differences in writing styles across authors. In contrast, fixing the number of characters by definition assures that the length of the input, and hence to a lesser extent the number of words (which is proportional to number of characters) is (more) constant. We assume that a more constant number of words also corresponds to a more constant amount of information in the input. We believe that this more constant amount of input information to predict from makes the learning easier, more so due to the small size of the training set.