基于双向长短期记忆网络和标签嵌入的文本分类模型

A text classification model based on BiLSTM and label embedding

提出了一种基于双向长短期记忆网络和标签嵌入的文本分类模型。首先利用BERT模型提取句子特征,然后通过BiLSTM和注意力机制得到融合重要上、下文信息的文本表示,最后将标签和词在联合空间学习,利用标签与词之间的兼容性得分对标签和句子表示加权,实现标签信息的双重嵌入,分类器根据给定标签信息对句子进行分类。在5个权威数据集上的实验表明,该方法能有效地提高文本分类性能,具有更好的实用性。

A text classification model based on BiLSTM and label embedding for text classification was proposed.Firstly,our method introduced the BERT model to extract high-quality sentence features.Then,we used BiLSTM and attention mechanism to get the text representations that integrate important context information.Finally,labels and words learned in the joint space,and we used the compatibility score deriving from the label-word pairs to weight the labels and sentences representations,realizing the double label embeddings.The classifier classifies sentences according to the given label information.Experimental results on five general authoritative datasets show that our method effectively improves the text classification performance,and our model has better practicability.