Chinese Abrupt Event Recognition Based on CBiGRU-Att Model

Abstract Aiming at the problem of poor portability of traditional event recognition methods, the need for a large number of learning features, and the poor interpretability of recurrent neural networks in different information features about degrees of importance, this paper proposes a Chinese abrupt event recognition method based CBiGRU-Att model. Firstly, the text corpus was preprocessed.Word2vec was used to generate word vectors and the local features of the word vectors were extracted by using the convolutional layer. Then the extracted features were used as the input of the BiGRU to obtain higher-order context features, and introduced the attention mechanism to weight feature. Finally, softmax function was used to activate the learned features and output the recognition results. Simulation results show that this method is superior to other methods in the precision and recall rate for Chinese abrupt event recognition.