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8 November 2023 A new RoBERTa-based criminal case recommendation method
Zhiyuan Xie
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Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 1292317 (2023) https://doi.org/10.1117/12.3011349
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China
Abstract
This paper proposes a new criminal case recommendation method based on RoBERTa and BiLSTM for the similarity matching task of Chinese judicial documents. The method first uses the UIE model to extract entities, relations, events, sentiments and other information from the judicial documents as the semantic representation of the documents. Then, it uses the pre-trained RoBERTa model to encode the semantic representation of the documents and obtain the semantic vectors of the documents. Next, it uses the BiLSTM model to align and fuse the semantic vectors of two documents and obtain the similarity vector of the documents. Finally, it uses cosine similarity to output the most similar cases. The paper conducts experiments on a self-built dataset and shows that the proposed method outperforms the commonly used traditional models on the criminal case recommendation task, with high accuracy and recall, and can effectively improve the quality and efficiency of case recommendation.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Zhiyuan Xie "A new RoBERTa-based criminal case recommendation method", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 1292317 (8 November 2023); https://doi.org/10.1117/12.3011349
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KEYWORDS
Data modeling
Deep learning
Machine learning
Neural networks
Feature extraction
Intelligence systems
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