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Pattern Recognition and Artificial Intelligence  2022, Vol. 35 Issue (2): 185-192    DOI: 10.16451/j.cnki.issn1003-6059.202202009
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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

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Abstract  

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.

Key wordsMulti-label Classification      Document Representation      Graph Convolutional Neural Network      Label Attention Mechanism      Label Relation     
Received: 07 May 2021     
ZTFLH: TP 391  
Fund:

Supported by National Natural Science Foundation of China(No.62072294,62076158,62106130,61906112), Key Research and Development Program of Shanxi Province(No.201803D421024), Graduate Innovation Programs of Shanxi Province(No.2021Y149)

Corresponding Authors: LI Deyu, Ph.D., professor. His research interests include gra-nular computing and machine learning.   
About author:: SONG Zeyu, master student. His research interests include text mining and natural language processing.LI Yang, Ph.D., lecturer. Her research interests include text sentiment analysis.WANG Suge, Ph.D., professor. Her research interests include natural language processing and sentiment analysis
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SONG Zeyu
LI Yang
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Cite this article:   
SONG Zeyu,LI Yang,LI Deyu等. Multi-label Classification of Legal Text with Fusion of Label Relations[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(2): 185-192.
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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202202009      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2022/V35/I2/185
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