Neural Models for Documents with Metadata
Table 2: Accuracy of various models on three datasets with categorical labels.
['[EMPTY]', '20news', 'IMDB', 'Yahoo']
Although scholar is comparable to the logistic regression baseline, our purpose here is not to attain state-of-the-art performance on text classification. Rather, the high accuracies we obtain demonstrate that we are learning low-dimensional representations of documents that are relevant to the label of interest, outperforming SLDA, and have the same attractive properties as topic models. Further, any neural network that is successful for text classification could be incorporated into fy and trained end-to-end along with topic discovery.