Abstract
This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several largescale 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.
| Original language | English (US) |
|---|---|
| Title of host publication | Advances in Neural Information Processing Systems |
| Publisher | Neural information processing systems foundation |
| Pages | 649-657 |
| Number of pages | 9 |
| Volume | 2015-January |
| State | Published - 2015 |
| Event | 29th Annual Conference on Neural Information Processing Systems, NIPS 2015 - Montreal, Canada Duration: Dec 7 2015 → Dec 12 2015 |
Other
| Other | 29th Annual Conference on Neural Information Processing Systems, NIPS 2015 |
|---|---|
| Country | Canada |
| City | Montreal |
| Period | 12/7/15 → 12/12/15 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing
Cite this
Character-level convolutional networks for text classification. / Zhang, Xiang; Zhao, Junbo; LeCun, Yann.
Advances in Neural Information Processing Systems. Vol. 2015-January Neural information processing systems foundation, 2015. p. 649-657.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Character-level convolutional networks for text classification
AU - Zhang, Xiang
AU - Zhao, Junbo
AU - LeCun, Yann
PY - 2015
Y1 - 2015
N2 - This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several largescale 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.
AB - This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several largescale 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.
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M3 - Conference contribution
VL - 2015-January
SP - 649
EP - 657
BT - Advances in Neural Information Processing Systems
PB - Neural information processing systems foundation
ER -