Character-level Convolutional Networks for Text ClassificationAn early version of this work entitled “Text Understanding from Scratch” was posted in Feb 2015 as arXiv:1502.01710. The present paper has considerably more experimental results and a rewritten introduction.
Table 4: Testing errors of all the models. Numbers are in percentage. “Lg” stands for “large” and “Sm” stands for “small”. “w2v” is an abbreviation for “word2vec”, and “Lk” for “lookup table”. “Th” stands for thesaurus. ConvNets labeled “Full” are those that distinguish between lower and upper letters
['Model', 'AG', 'Sogou', 'DBP.', 'Yelp P.', 'Yelp F.', 'Yah. A.', 'Amz. F.', 'Amz. P.']
Note that since we do not have a Chinese thesaurus, the Sogou News dataset does not have any results using thesaurus augmentation. We labeled the best result in blue and worse result in red. Each of these plots is computed by taking the difference between errors on comparison model and our character-level ConvNet model, then divided by the comparison model error. All ConvNets in the figure are the large models with thesaurus augmentation respectively. Comparing with traditional models, this suggests such a simple use of a distributed word representation may not give us an advantage to text classification. However, our experiments does not speak for any other language processing tasks or use of word2vec in any other way.