计算机工程与应用2019，Vol.55Issue(5)：135-142,8.DOI:10.3778/j.issn.1002-8331.1803-0090
字符级卷积神经网络短文本分类算法
Character-Level Convolutional Neural Networks for Short Text Classification
摘要
Abstract
Since short text is characterized of the short length, sparse features and strong context dependency, the tradi-tional models have a limited precision. Motivated by this, this article offers an empirical exploration on a character-level model which implements a combination of Convolutional Neural Network(CNN)and Long Short-Term Memory neural networks(LSTM)for short text classification. Including the highway networks framework so that it can address the difficult of training and improve the accuracy of classification. The evaluations on several datasets show that the proposed model outperforms the traditional and CNN-based models on short text classification mission.关键词
字符级/神经网络/文本分类/高速公路网络Key words
character-level/ neural network/ text classification/ highway networks分类
信息技术与安全科学引用本文复制引用
刘敬学,孟凡荣,周勇,刘兵..字符级卷积神经网络短文本分类算法[J].计算机工程与应用,2019,55(5):135-142,8.基金项目
国家自然科学基金(No.61601175). （No.61601175）
