BiGRU结合注意力机制的文本分类研究

Research on Text Classification Based on BiGRU Combined with Attention Mechanism

随着信息时代的发展,文本包含的信息量越来越多,而同一段文本可以隶属于不同的类别,为了提升多标签文本分类任务的准确率,提出了一种基于ALBERT预训练、双向GRU并结合注意力机制的改进的多标签文本分类模型——BiGRU-Att模型。在百度发布的中文事件抽取数据集上进行实验,该模型的识别准确率达到了99.68%,相对比较组的BiLSTM-Att、LSTM-Att、BiGRU、BiLSTM、LSTM等模型的测试结果,准确率更高,性能更优。实验结果表明,改进的BiGRU-Att模型能有效提升多标签文本分类任务的准确率。

With the development of information age,the amount of information contained in texts is increasing.It is often the case that the same text can belong to different categories.In order to improve the accuracy of multi-label text classification,BiGRU-Att model,an improved multi-label text classification model,is proposed based on ALBERT pre-training,BiGRU combined with attention mechanism.Experiments on Chinese event extraction data set published by Baidu show that the recognition accuracy of the model reaches 99.68%.Compared with the test results of BiLSTM-Att,LSTM-Att,BiGRU,BiLSTM,LSTM and other models of the comparison group,the model has higher accuracy and better performance.Experimental results show that the improved BiGRU-Att model can effectively improve the accuracy of multi-label text classification.