基于BLSTM-Attention神经网络模型的化工事故分类

化工事故新闻数据包含新闻内容，标题以及新闻来源等方面信息，新闻内容的文本对上下文具有较强的依赖性.为了更准确地提取文本特征并提高化工事故分类的准确性，该文提出了一种基于Attention机制的双向LSTM （BLSTM-Attention）神经网络模型对化工新闻文本进行特征提取并实现文本分类.BLSTM-Attention神经网络模型能够结合文本上下文语义信息，通过正向和反向的角度来提取事故新闻的文本特征；考虑到事故新闻中不同词对文本的贡献不大相同，加入Attention机制对不同词和句子分配不同权重.最后，将该文提出的分类方法与Naive-Bayes、CNN、RNN、BLSTM分类方法在相同的化工事故新闻数据集上进行实验对比.实验结果表明：该文提出的神经网络模型BLSTM-Attention神在化工数据集上的效果更优于其他分类方法模型.

Chemical Accident Classification Based on BLSTM-Attention Neural Network Model

Chemical accident news data contains information such as news content, titles, and news sources. The text of news content is highly dependent on the context. In order to extract text features more accurately and improve the accuracy of chemical accident classification, this study proposes a Bidirectional LSTM (BLSTM-Attention) neural network model based on Attention mechanism to extract features of chemical news texts and realize text classification. The BLSTM-Attention neural network model can combine text context semantic information to extract text features of accident news through forward and reverse angles. Considering that different words have different contributions to the text in the accident news, the Attention mechanism is added to assign different weights to different words and sentences. Finally, the proposed classification method is compared with Naive-Bayes, CNN, RNN, BLSTM classification method on the same chemical accident news data set. Experimental results show that the BLSTM-Attention neural network model proposed in this study is better than other classification models in chemical data set.