<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In ample data justice, predicting legal outcomes and identifying similar cases hold significant value. This paper presents an advanced legal prediction algorithm that integrates the specific features of legal texts. Utilizing the Text Rank model, it extracts essential text features from legal provisions and facts, enabling the precise deployment of legal requirements based on detailed case analyses and legal knowledge. To overcome the hurdles of scant training data and the challenge of distinguishing similar legal documents, we developed a similar case matching model employing twin Bert encoders. Our empirical study reveals theft, intentional injury, and fraud as the predominant crimes, with sample counts of 335,745, 174,526, and 47,677, respectively. These top offenses, correlating with the most frequently cited laws, account for 85.79% of our dataset. The analysis further indicates “RMB” as the most recurring word in theft and fraud cases, and “minor injury” in intentional injury instances. Notably, our findings show that categories such as “misappropriation” are prone to misclassification as “embezzlement,” and “robbery” often gets confused with “theft,” highlighting the complexities of legal classification.</p>
</abstract>

In ample data justice, predicting legal outcomes and identifying similar cases hold significant value. This paper presents an advanced legal prediction algorithm that integrates the specific features of legal texts. Utilizing the Text Rank model, it extracts essential text features from legal provisions and facts, enabling the precise deployment of legal requirements based on detailed case analyses and legal knowledge. To overcome the hurdles of scant training data and the challenge of distinguishing similar legal documents, we developed a similar case matching model employing twin Bert encoders. Our empirical study reveals theft, intentional injury, and fraud as the predominant crimes, with sample counts of 335,745, 174,526, and 47,677, respectively. These top offenses, correlating with the most frequently cited laws, account for 85.79% of our dataset. The analysis further indicates “RMB” as the most recurring word in theft and fraud cases, and “minor injury” in intentional injury instances. Notably, our findings show that categories such as “misappropriation” are prone to misclassification as “embezzlement,” and “robbery” often gets confused with “theft,” highlighting the complexities of legal classification.

Exploring the Path of Judicial Big Data to Enhance Data Governance Capability

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Exploring the Path of Judicial Big Data to Enhance Data Governance Capability"}

In ample data justice, predicting legal outcomes and identifying similar cases hold significant value. This paper presents an advanced legal prediction...

Exploring the Path of Judicial Big Data to Enhance Data Governance Capability

Data publikacji: 03 maj 2024

Zakres stron: -

Otrzymano: 08 kwi 2024

Przyjęty: 26 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1015

Słowa kluczowe
Legal Prediction, Big Data Justice, Similar Cases, Text Rank Model, Twin Bert

© 2024 Yingshuai Liang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring the Path of Judicial Big Data to Enhance Data Governance Capability

Data publikacji: 03 maj 2024

Zakres stron: -

Otrzymano: 08 kwi 2024

Przyjęty: 26 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1015

Słowa kluczoweLegal Prediction, Big Data Justice, Similar Cases, Text Rank Model, Twin Bert

© 2024 Yingshuai Liang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Legal Prediction, Big Data Justice, Similar Cases, Text Rank Model, Twin Bert