Papers › Character-level Convolutional Networks for Text Classification
Character-level Convolutional Networks for Text Classification
Xiang Zhang, Junbo Zhao, Yann Lecun
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.
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Code
Syntology Ran 4 of 20 code samples harvested from 6 repositories linked to this paper; 16 have no recorded run. Of those that ran: 2 ran · our draft was wrong; 2 ran · fixture could not drive it.
By repository: community (archive-listed): 18 samples from 6 repositories, 3 ran; 2 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
30 repositories listed; official and paper-mentioned ones first.
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
20 samples harvested; 4 ran; 0 honoured the contract we drafted; 16 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 5 of the 20 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 6 repositories linked to this paper, official or community; each sample names its own and says which. Some samples are identical code Syntology first harvested from another repository; for those, this paper's copy is not located and its licence is not recorded. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentiment Analysis | Yelp Binary classification | Char-level CNN | Error | 4.88 | #20 of 20 | Archive leaderboard | report |
| Sentiment Analysis | Yelp Fine-grained classification | Char-level CNN | Error | 37.95 | #16 of 17 | Archive leaderboard | report |
| Text Classification | AG News | Char-level CNN | Error | 9.51 | #19 of 24 | Archive leaderboard | report |
| Text Classification | DBpedia | Char-level CNN | Error | 1.55 | #20 of 21 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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