融合标签关系的法律文本多标签分类方法

随着大数据技术的快速发展,多标签文本分类在司法领域也催生出诸多应用.在法律文本中通常存在多个要素标签,标签之间往往具有相互依赖性或相关性,准确识别这些标签需要多标签分类方法的支持.因此,文中提出融合标签关系的法律文本多标签分类方法.方法构建标签的共现矩阵,利用图卷积网络捕捉标签之间的依赖关系,并结合标签注意力机制,计算法律文本和标签每个词的相关程度,得到特定标签的法律文本语义表示.最后,融合标签图构建的依赖关系和特定标签的法律文本语义表示,对文本进行综合表示,实现文本的多标签分类.在法律数据集上的实验表明,文中方法获得较好的分类精度和稳定性.

Multi-label Classification of Legal Text with Fusion of Label Relations

With the rapid development of big data technology,multi-label text classification spawns many applications in the judicial field.There are multiple element labels in legal texts,and the labels are interdependent or correlated.Accurate identification of these labels requires the support of multi-label classification method.In this paper,a multi-label classification method of legal texts with fusion of label relations(MLC-FLR)is proposed.A graph convolution network model is utilized to capture the dependency relationship between labels by constructing the co-occurrence matrix of labels.The label attention mechanism is employed to calculate the degrees of correlation between a legal text and each label word,and the legal text semantic representation of a specific label can be obtained.Finally,the comprehensive representation of a text for multi-label classification is carried out by combining the dependency relationship and the legal text semantic representation of a specific label.Experimental results on the legal text datasets show that MLC-FLR achieves better classification accuracy and stability.