This article explores the use of character-level convolutional networks (ConvNets) for text classification, demonstrating their effectiveness through empirical studies. The authors constructed large-scale datasets to show that character-level ConvNets can achieve state-of-the-art or competitive results compared to traditional models like bag-of-words, n-grams, and their TFIDF variants, as well as deep learning models such as word-based ConvNets and recurrent neural networks (RNNs). The key contributions include: 1. **Character-Level ConvNets**: The authors designed two ConvNets, one large and one small, both with 9 layers (6 convolutional and 3 fully-connected layers). These models were trained on large-scale datasets and showed superior performance. 2. **Data Augmentation**: They used data augmentation techniques, specifically replacing words or phrases with their synonyms from an English thesaurus, to improve model generalization. 3. **Large-Scale Datasets**: Several large-scale datasets were created, including news articles, DBPedia ontology, Yelp reviews, Yahoo! Answers, and Amazon reviews, to evaluate the models' performance. 4. **Comparative Analysis**: The models were compared against traditional methods (bag-of-words, bag-of-ngrams, bag-of-means on word embedding) and deep learning methods (word-based ConvNets, RNNs). The results showed that character-level ConvNets performed well, especially on larger datasets and user-generated texts. 5. **Discussion**: The study found that character-level ConvNets can work effectively without the need for word-level features, suggesting that language can be treated as a raw signal. The choice of alphabet, dataset size, and task type also significantly influenced the performance of the models. 6. **Conclusion**: The article concludes that character-level ConvNets are a powerful method for text classification, but their effectiveness depends on various factors. Future work aims to apply these models to a broader range of language processing tasks, particularly those requiring structured outputs. The research was supported by NVIDIA Corporation and Amazon.com Inc.This article explores the use of character-level convolutional networks (ConvNets) for text classification, demonstrating their effectiveness through empirical studies. The authors constructed large-scale datasets to show that character-level ConvNets can achieve state-of-the-art or competitive results compared to traditional models like bag-of-words, n-grams, and their TFIDF variants, as well as deep learning models such as word-based ConvNets and recurrent neural networks (RNNs). The key contributions include: 1. **Character-Level ConvNets**: The authors designed two ConvNets, one large and one small, both with 9 layers (6 convolutional and 3 fully-connected layers). These models were trained on large-scale datasets and showed superior performance. 2. **Data Augmentation**: They used data augmentation techniques, specifically replacing words or phrases with their synonyms from an English thesaurus, to improve model generalization. 3. **Large-Scale Datasets**: Several large-scale datasets were created, including news articles, DBPedia ontology, Yelp reviews, Yahoo! Answers, and Amazon reviews, to evaluate the models' performance. 4. **Comparative Analysis**: The models were compared against traditional methods (bag-of-words, bag-of-ngrams, bag-of-means on word embedding) and deep learning methods (word-based ConvNets, RNNs). The results showed that character-level ConvNets performed well, especially on larger datasets and user-generated texts. 5. **Discussion**: The study found that character-level ConvNets can work effectively without the need for word-level features, suggesting that language can be treated as a raw signal. The choice of alphabet, dataset size, and task type also significantly influenced the performance of the models. 6. **Conclusion**: The article concludes that character-level ConvNets are a powerful method for text classification, but their effectiveness depends on various factors. Future work aims to apply these models to a broader range of language processing tasks, particularly those requiring structured outputs. The research was supported by NVIDIA Corporation and Amazon.com Inc.