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

<p>In this paper, we use big data analysis techniques combined with the TF-IDF algorithm to weigh the frequently occurring word frequency vectors in text and reduce the document length to obtain keywords without destroying the original text feature information. The similarity of text features is combined with a Bayesian algorithm for label classification to facilitate data query and indexing. The results show that the running time of the system is kept around 14s, the recall and accuracy can be close to about 75% and 72% on average, and the number of keywords can reach 5971 with an F1 value of 0.9, which proves the effectiveness of the artificial intelligence legal regulation system based on big data analysis.</p>
</abstract>

In this paper, we use big data analysis techniques combined with the TF-IDF algorithm to weigh the frequently occurring word frequency vectors in text and reduce the document length to obtain keywords without destroying the original text feature information. The similarity of text features is combined with a Bayesian algorithm for label classification to facilitate data query and indexing. The results show that the running time of the system is kept around 14s, the recall and accuracy can be close to about 75% and 72% on average, and the number of keywords can reach 5971 with an F1 value of 0.9, which proves the effectiveness of the artificial intelligence legal regulation system based on big data analysis.

Dilemmas and Breakthroughs in the Legal Regulation of Artificial Intelligence Based on Deep Learning Models

Applied Mathematics and Nonlinear Sciences

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

In this paper, we use big data analysis techniques combined with the TF-IDF algorithm to weigh the frequently occurring word frequency vectors in text and...

Dilemmas and Breakthroughs in the Legal Regulation of Artificial Intelligence Based on Deep Learning Models

Online veröffentlicht: 09. Okt. 2023

Seitenbereich: -

Eingereicht: 14. Dez. 2022

Akzeptiert: 19. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00561

Schlüsselwörter
Text features, TF-IDF algorithm, Bayesian algorithm, Legal regulation

© 2023 Yanggui Li, published by Sciendo

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

Dilemmas and Breakthroughs in the Legal Regulation of Artificial Intelligence Based on Deep Learning Models

Online veröffentlicht: 09. Okt. 2023

Seitenbereich: -

Eingereicht: 14. Dez. 2022

Akzeptiert: 19. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00561

SchlüsselwörterText features, TF-IDF algorithm, Bayesian algorithm, Legal regulation

© 2023 Yanggui Li, published by Sciendo

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

Schlüsselwörter
Text features, TF-IDF algorithm, Bayesian algorithm, Legal regulation