Multi-label Classification of Legal Text with Fusion of Label Relations
SONG Zeyu1, LI Yang2, LI Deyu1,3, WANG Suge1,3
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006;
2. School of Finance, Shanxi University of Finance and Economics, Taiyuan 030006;
3. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
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.
宋泽宇, 李旸, 李德玉, 王素格. 融合标签关系的法律文本多标签分类方法[J]. 模式识别与人工智能, 2022, 35(2): 185-192.
SONG Zeyu, LI Yang, LI Deyu, WANG Suge. Multi-label Classification of Legal Text with Fusion of Label Relations. Pattern Recognition and Artificial Intelligence, 2022, 35(2): 185-192.
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2022, Vol. 35 
