Character-Level Convolutional Networks for Text Classification
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
Text
Zhang et al. "Character-Level Convolutional Networks for Text Classification." Neural Information Processing Systems, 2015.Markdown
[Zhang et al. "Character-Level Convolutional Networks for Text Classification." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/zhang2015neurips-characterlevel/)BibTeX
@inproceedings{zhang2015neurips-characterlevel,
title = {{Character-Level Convolutional Networks for Text Classification}},
author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {649-657},
url = {https://mlanthology.org/neurips/2015/zhang2015neurips-characterlevel/}
}