Character-level convolutional networks for text classification
Advances in neural information processing systems, 2015•proceedings.neurips.cc
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.
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.
Abstract
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.
proceedings.neurips.cc