ALBERT与双向GRU 的多标签灾情信息预测

针对灾害求助信息辨识不准确、响应能力不足的问题,本文提出了基于ALBERT和双向GRU的文本多标签分类模型。首先利用ALBERT预处理语言模型对文本信息进行编码,获取文本的动态词特征向量,并送入双向GRU神经网络进行训练,根据不同的单词赋予不同的权重,应用Attention机制进行解码。利用模拟退火算法求解最优阈值,以微平均值作为评价函数,确定样本的标签类别归属。与逻辑回归、朴素贝叶斯和LSTM长短期记忆神经网络等模型进行比较,结果显示,多标签分类模型具有更高的准确率,达到95%,汉明损失仅到0.05,能够更好地辨别灾情求助信息,提高救援效率。

Study on multi-tag disaster information prediction based on ALBERT and bidirectional GRU

Aiming at the problems of inaccurate identification and insufficient response ability of disaster help information, this paper proposes a text multi label classification model based on ALBERT and bidirectional GRU. Firstly, the text information is encoded by using the ALBERT preprocessing language model, and the dynamic word feature vector of the text is obtained. Then, it is sent to the bidirectional GRU neural network for training. According to different words, different weights are given, and the attention mechanism is used for decoding. The simulated annealing algorithm is used to solve the optimal threshold, and the micro average value is used as the evaluation function to determine the label category of samples. Compared with Logistic regression, Naive Bayes and Long-term and short-term memory neural network (LSTM), the results show that the multi label classification model has a higher accuracy rate of 95%, and the Hamming loss is only 0.05, which can better identify the disaster information and improve the rescue efficiency.