Interpretable Text Classification Using CNN and Max-pooling
Table 1: Accuracy[%] on classification tasks. out methods* divide the input text into sentences and employs the proposed multi-sentence weighting strategy for training.
['Model', 'R8', 'R52', 'TREC', 'Obsumed', '20NG']
From the results we can see that our methods achieve great performance in most of the datasets. Our methods show poor performance on the dataset TREC, this may be because the scale of TREC is small and our method does not use the pre-trained word vectors. In Obsumed and 20NG, the multi-sentence weighting strategy effectively promotes the performance of the model.