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

<p>In the information age, the traditional legal supervision system has exposed many problems in practice, and has been unable to meet the needs of the development of governance modernization. The text of the legal supervision system is analyzed using text feature extraction to examine the supervisory content and effect. The TF-IDF algorithm is used to set the highest threshold and the lowest threshold to reduce the dimensionality of the feature items in the text of the legal supervision and improve the efficiency of text feature extraction. Add TextRank algorithm to annotate the lexical properties in the legal supervision system, remove the deactivated words, and sort the keyword PR values of the nodes to derive the most important keywords. The LDA model is used to divide the subject of legal supervision text, and the corresponding optimization strategy is explored according to the current situation of legal supervision content and the supervision effect under different supervision subjects. The results show that the most influential factor in state supervision is the soundness of legal documents, with an impact score of 4.1586, and the grade in the effectiveness score of the soundness of legal documents is average, indicating that state supervision still needs to be strengthened in the piece of soundness of legal documents. The effectiveness score of supervision awareness in the effectiveness score of social supervision is 50.0694, which indicates that the cultivation of supervision awareness should be emphasized in the legal supervision system.</p>
</abstract>

In the information age, the traditional legal supervision system has exposed many problems in practice, and has been unable to meet the needs of the development of governance modernization. The text of the legal supervision system is analyzed using text feature extraction to examine the supervisory content and effect. The TF-IDF algorithm is used to set the highest threshold and the lowest threshold to reduce the dimensionality of the feature items in the text of the legal supervision and improve the efficiency of text feature extraction. Add TextRank algorithm to annotate the lexical properties in the legal supervision system, remove the deactivated words, and sort the keyword PR values of the nodes to derive the most important keywords. The LDA model is used to divide the subject of legal supervision text, and the corresponding optimization strategy is explored according to the current situation of legal supervision content and the supervision effect under different supervision subjects. The results show that the most influential factor in state supervision is the soundness of legal documents, with an impact score of 4.1586, and the grade in the effectiveness score of the soundness of legal documents is average, indicating that state supervision still needs to be strengthened in the piece of soundness of legal documents. The effectiveness score of supervision awareness in the effectiveness score of social supervision is 50.0694, which indicates that the cultivation of supervision awareness should be emphasized in the legal supervision system.

Optimization of the Improvement Path of Legal Supervision System in the Information Age

Applied Mathematics and Nonlinear Sciences

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

In the information age, the traditional legal supervision system has exposed many problems in practice, and has been unable to meet the needs of the...

Optimization of the Improvement Path of Legal Supervision System in the Information Age

Data publikacji: 31 sty 2024

Zakres stron: -

Otrzymano: 20 gru 2023

Przyjęty: 25 gru 2023

DOI: https://doi.org/10.2478/amns-2024-0213

Słowa kluczowe
LDA topic model, TF-IDF algorithm, TextRank algorithm, Legal supervision system

© 2024 Zhao Wang et al., published by Sciendo

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

Optimization of the Improvement Path of Legal Supervision System in the Information Age

Data publikacji: 31 sty 2024

Zakres stron: -

Otrzymano: 20 gru 2023

Przyjęty: 25 gru 2023

DOI: https://doi.org/10.2478/amns-2024-0213

Słowa kluczoweLDA topic model, TF-IDF algorithm, TextRank algorithm, Legal supervision system

© 2024 Zhao Wang et al., published by Sciendo

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

Słowa kluczowe
LDA topic model, TF-IDF algorithm, TextRank algorithm, Legal supervision system