%0 Book Section %1 zhang2015characterlevel %A Zhang, Xiang %A Zhao, Junbo %A LeCun, Yann %B Advances in Neural Information Processing Systems 28 %D 2015 %E Cortes, C. %E Lawrence, N. D. %E Lee, D. D. %E Sugiyama, M. %E Garnett, R. %I Curran Associates, Inc. %K classification cnn ml nips text understanding %P 649--657 %T Character-level Convolutional Networks for Text Classification %U http://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf %X 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.

@incollection{zhang2015characterlevel, 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.}, added-at = {2018-09-26T11:55:16.000+0200}, author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann}, biburl = {https://www.bibsonomy.org/bibtex/230d6b77d66b0aa0ec6f587eb76e963b0/jaeschke}, booktitle = {Advances in Neural Information Processing Systems 28}, editor = {Cortes, C. and Lawrence, N. D. and Lee, D. D. and Sugiyama, M. and Garnett, R.}, interhash = {ec817986f91d2662694825ce51f3faa8}, intrahash = {30d6b77d66b0aa0ec6f587eb76e963b0}, keywords = {classification cnn ml nips text understanding}, pages = {649--657}, publisher = {Curran Associates, Inc.}, timestamp = {2018-09-26T11:55:16.000+0200}, title = {Character-level Convolutional Networks for Text Classification}, url = {http://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf}, year = 2015 }