Character-level Convolutional Networks for Text Classification
Machine Learning
2016-04-05 v3 Computation and Language
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.
Cite
@article{arxiv.1509.01626,
title = {Character-level Convolutional Networks for Text Classification},
author = {Xiang Zhang and Junbo Zhao and Yann LeCun},
journal= {arXiv preprint arXiv:1509.01626},
year = {2016}
}
Comments
An 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, Advances in Neural Information Processing Systems 28 (NIPS 2015)