Character-level Convolutional Networks for Text Classification

2015/09/04 by Xiang Zhang, Junbo Zhao, Zhang, Xiang +3 · 1 voice · 354 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Text and Document Classification Technologies #Topic Modeling #cs.CL #cs.LG

paper · pdf · doi:10.48550/arxiv.1509.01626

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)

openalex publication_date 2015/09/04 · arxiv published 2015/09/04 · arxiv created 2016/04/04 · arxiv updated 2016/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• On the difficulty of training Recurrent Neural Networks
• Improving neural networks by preventing co-adaptation of feature detectors
• Long Short-Term Memory
• Gradient-based learning applied to document recognition
• A STATISTICAL INTERPRETATION OF TERM SPECIFICITY AND ITS APPLICATION IN RETRIEVAL
• Backpropagation Applied to Handwritten Zip Code Recognition
• Text Understanding from Scratch

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Discussions

• Character-Level Convolutional Networks for Text Classification∗ [hn, 2 points, 0 comments]

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