Character-level convolutional networks for text classification

Xiang Zhang; Junbo Zhao; Yann Lecun

Character-level convolutional networks for text classification

Xiang Zhang, Junbo Zhao, Yann Lecun

Computer Science

Research output: Contribution to journal › Conference article › peer-review

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)
Pages (from-to)	649-657
Number of pages	9
Journal	Advances in Neural Information Processing Systems
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

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

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title = "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 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.",

author = "Xiang Zhang and Junbo Zhao and Yann Lecun",

year = "2015",

language = "English (US)",

volume = "2015-January",

pages = "649--657",

journal = "Advances in Neural Information Processing Systems",

issn = "1049-5258",

note = "29th Annual Conference on Neural Information Processing Systems, NIPS 2015 ; Conference date: 07-12-2015 Through 12-12-2015",

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T1 - Character-level convolutional networks for text classification

AU - Zhang, Xiang

AU - Zhao, Junbo

AU - Lecun, Yann

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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 article

AN - SCOPUS:84965162393

SN - 1049-5258

VL - 2015-January

SP - 649

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JO - Advances in Neural Information Processing Systems

JF - Advances in Neural Information Processing Systems

T2 - 29th Annual Conference on Neural Information Processing Systems, NIPS 2015

Y2 - 7 December 2015 through 12 December 2015

ER -

Character-level convolutional networks for text classification

Abstract

ASJC Scopus subject areas

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