Abstract: Aiming at the characteristics of sparseness and too limited words in one short text, in order to better deal with the problem of short text classification, we proposed a short text classification model based on integrated neural networks. Firstly, the extended word vector was used as the input of the model, so that the numerical word vector could effectively describe the morphological, syntactic and semantic features of short text. Secondly, the recurrent neural network (RNN) was used to model the semantics of short text, capture the dependency of internal structure of short text. Finally, we used the regularization term to select the model with minimal empirical risk and model complexity simultaneously in the process of training model. By the short text classification experiments on the corpus, we verified  that the proposed model has a better classification effect, and the classification model could deal with short text input with variable length, and has a good robustness.

Key words: short text, classification, extended word vector, integrated neural network