来自雅虎 Yahoo! Answers Comprehensive Questions and Answers1.0 数据集的 10 个主要分类数据。每个类 别分别包含 140,000 个训练样本和 5,000 个测试样本。
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.
译：
本文对字符级卷积网络（ConvNets）在文本分类中的应用进行了实证研究。我们构建了几个大规模的数据集，以证明字符级卷积网络可以达到最先进或最具竞争力的结果。比较了传统模型，如单词包、n-grams及其TFIDF变体，以及基于单词的ConvNets和递归神经网络等深度学习模型。
大家可以到官网地址下载数据集，我自己也在百度网盘分享了一份。可关注本人公众号，回复“2020082503”获取下载链接。
只要自己有时间，都尽量写写文章，与大家交流分享。
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