关键词: 字符级, 神经网络, 文本分类, 高速公路网络

Abstract: Since short text is characterized of the short length, sparse features and strong context dependency, the traditional 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.

Key words: character-level, neural network, text classification, highway networks