Character-level Convolutional Networks for Text Classification
This work addresses text classification for natural language processing applications, presenting an incremental approach by applying convolutional networks at the character level.
The authors tackled text classification by exploring character-level convolutional networks, achieving state-of-the-art or competitive results on large-scale datasets.
This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.