Abstract
Current engines search based on keywords, but in the process of legal case search recommendation, only some keywords are not enough. This work mainly completes the classification task of legal texts in three aspects: crime prediction, articles of law recommendation, and prison term prediction. We use TextCNN as a baseline. First of all, we make different attempts on the receptive field of the convolutional layer to verify its impact on the task of this project. Secondly, in order to better reflect the impact of context on the task of this project, we combined TextCNN and bidirectional GRU or Attention. In order to verify the effect of the above method, we use the CAIL2018 dataset for verification. The dataset contains more than 2.6 million cases. The experimental results show that the change of the receptive field and the introduction of attention have a specific impact on this project, and the combination of TextCNN and Attention has also achieved good results for the comprehensive performance of the three tasks of this work.
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Zhou, J., Li, Y., Zhang, B., Chen, X., Wei, X., Sun, X. (2023). A Fast Method of Legal Decision Recommendation System. In: Yang, S., Islam, S. (eds) Web and Big Data. APWeb-WAIM 2022 International Workshops. APWeb-WAIM 2022. Communications in Computer and Information Science, vol 1784. Springer, Singapore. https://doi.org/10.1007/978-981-99-1354-1_22
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