Open AccessRecognition of Chinese Legal Elements Based on Transfer Learning and Semantic Relevance
Author(s) -
Dian Zhang,
Hewei Zhang,
Long Wang,
Jiamei Cui,
Wen Zheng
Publication year - 2022
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1155/2022/1783260
Subject(s) - computer science , similarity (geometry) , relevance (law) , task (project management) , identification (biology) , field (mathematics) , artificial intelligence , semantic similarity , element (criminal law) , information retrieval , transfer of learning , natural language processing , data science , law , political science , image (mathematics) , botany , mathematics , management , pure mathematics , economics , biology
In recent years, LegalAI has rapidly attracted the attention of AI researchers and legal professionals alike. Elements of LegalAI are known as legal elements. These elements can bring intermediate supervisory information to the judicial trial task and make the model’s prediction results more interpretable. This paper proposes a Chinese legal element identification method based on BERT’s contextual relationship capture mechanism to identify the elements by measuring the similarity between legal elements and case descriptions. On the China Law Research Cup 2019 Judicial Artificial Intelligence Challenge (CAIL-2019) dataset, the final result improves 4.2 points over the method based on the BERT model but without using similarity metrics. This research method makes full use of the semantic information of text, which is essential in the judicial field of document processing.
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