In view of the weak generalization of traditional event recognition methods, the limitation of dependence on field knowledge of expert, the longer train time of deep neural network, and the problem of gradient dispersion, the neural network joint model, Conv-RDBiGRU, integrated residual structure was proposed. Firstly, text corpus is preprocessed by word segmentation and stop words processing and uses word embedding to form the matrix of word vectors. Then, local semantic features are extracted through convolution operation, and deep context semantic features are extracted through RDBiGRU. Finally, the learned features are activated by softmax function and the recognition results are output. The novelty of work is that we integrate residual structure into recurrent neural network and combine these methods and field of application. The simulation results show that this method improves precision and recall of Chinese emergency event recognition, and the F-value is better than other methods.

中文翻译：

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使用Conv-RDBiGRU模型的中文紧急事件识别。

鉴于传统事件识别方法的通用性较弱，对专家领域知识的依赖限制，深层神经网络的训练时间较长以及梯度分散问题，因此，神经网络联合模型，Conv-RDBiGRU，集成残差提出了结构。首先，对文本语料库进行分词和停用词处理，并通过词嵌入形成词向量矩阵。然后，通过卷积运算提取局部语义特征，并通过RDBiGRU提取深度上下文语义特征。最后，通过softmax功能激活学习到的特征，并输出识别结果。新颖的工作是将残差结构集成到递归神经网络中，并将这些方法和应用领域相结合。F值优于其他方法。