Research Article
CharTeC-Net: An Efficient and Lightweight Character-Based Convolutional Network for Text Classification
Table 2
Accuracy results for all the models. Numbers are in percentage.
| Models | AG’s news | Sogou news | DBPedia | Yelp review polarity | Yelp review full | Yahoo! answers | Amazon review full | Amazon review polarity |
| Bag of words [19] | 88.8 | 92.8 | 96.6 | 92.2 | 58.0 | 68.9 | 54.6 | 90.4 | ngrams [19] | 92.0 | 97.0 | 98.6 | 95.6 | 56.3 | 68.5 | 54.3 | 92.0 | ngrams TFIDF [19] | 92.4 | 97.2 | 98.7 | 95.4 | 54.8 | 68.5 | 52.4 | 91.5 | Char-CNN [19] | 87.2 | 95.1 | 98.3 | 94.7 | 62.0 | 71.2 | 58.7 | 94.5 | fastText [8] | 92.5 | 96.8 | 98.6 | 95.7 | 63.9 | 72.3 | 60.2 | 94.6 | Char-CRNN [24] | 91.4 | 95.2 | 98.6 | 94.5 | 61.8 | 71.7 | 59.2 | 94.1 | VDCNN [22] | 91.3 | 96.8 | 98.7 | 95.7 | 64.7 | 73.4 | 63.0 | 95.7 | Naive Bayes [30] | 90.0 | 86.3 | 96.0 | 86.0 | 51.4 | 68.7 | — | — | Kneser-Ney Bayes [30] | 89.3 | 94.6 | 95.4 | 81.8 | 41.7 | 69.3 | — | — | MLP Naive Bayes [30] | 89.9 | 76.1 | 87.2 | 73.6 | 40.4 | 60.6 | — | — | Discriminative LSTM [30] | 92.1 | 94.9 | 98.7 | 92.6 | 59.6 | 73.7 | — | — | Generative LSTM-independent comp. [30] | 90.7 | 93.5 | 94.8 | 90.0 | 51.9 | 70.5 | — | — | Generative LSTM-shared comp. [30] | 90.6 | 90.3 | 95.4 | 88.2 | 52.7 | 69.3 | — | — | Char shallow-and-wide CNN [23] | 90.7 | — | 98.0 | 94.4 | 60.3 | 70.2 | — | — | Word shallow-and-wide CNN [23] | 92.2 | — | 98.7 | 95.9 | 64.9 | 73.0 | — | — | CharTeC-Net (our model) | 91.6 | 96.4 | 98.5 | 95.83 | 63.6 | 67.8 | 61.5 | 94.0 |
|
|