Character-Level neural networks for short text classification
Jing-Xue Liu, Fanrong Meng, Yong Zhou, Bing Liu · 2017
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 recurrent neural networks(RNN) for short text classification. Including the highway networks framework so that we can address the difficult of training and improve the accuracy of classification. The evaluations on several datasets showed that our proposed model outperforms the standard CNN and traditional models on short text classification mission.