Abstract
In recent years, the Supreme People’s Court of China has vigorously promoted the information construction of the people’s court. A large number of information are stored in judgment documents, these documents contain the basic information of cases and statutes cited in cases. As far as we know, there are very few studies on the recommendation of statutes at present. We study the method by which judges choose the statutes in practical work, linking details of cases with the contents of statutes and choosing appropriate statutes to support the trial. Through data research on a large number of judgment documents, we propose a method based on the causes of action and contents of recommendation statutes, and use the Random Forest algorithm for training. In our approach, we take key words of statutes as the basic characteristics, which are used for finding more key words of documents to set up a key words dictionary for each cause of action. We use the dictionary to vector documents and train a classifier for each cause of action. We build a data set for this task and carry out evaluation experiments on the data set. The experimental results show that our method can perform well.
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This work was supported by the National Key R&D Program of China (2016YFC0800803).
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Li, Z. et al. (2018). Predicting Statutes Based on Causes of Action and Content of Statutes. In: Zhou, Q., Miao, Q., Wang, H., Xie, W., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 902. Springer, Singapore. https://doi.org/10.1007/978-981-13-2206-8_39
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DOI: https://doi.org/10.1007/978-981-13-2206-8_39
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