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statutory text analytics program

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statutory text analytics program

James o “Niell et el [3] suggested a statutory text analytics program. It allows law students to interpret and analyze Uk laws and identify the particular legal concepts and words that could be correlated with enforcement through various publications. Many approaches for modeling topics are explored in this paper, such as Latent Semantic Indexing (LSI), Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and HDP Hierarchical Dirichlet Allocation. The legislative text of this work is used in the United Kingdom. Preprocessing is done for natural language processing. Theme analysis is carried out using factorization of the Negative Matrix and LDA. LDA has been found to have provided the highest outcomes.

 

 

Farzan et el[4] suggested methods of clustering and thematic analysis to evaluate the regional cyber (NC) defense policies. This research analyzed 60 NCSs through the implementation of approaches to machine learning, such as hierarchical clustering and thematic modeling. Throughout the study of contextual details such as textual rules, techniques, and regulations pinpointed computational approaches such as LDA and clustering may be used to provide a clearer image and perspectives through NCS formulation. This may be used as a complementary method to help decision leaders properly recognize issues that are overlooked or not adequately addressed. General consistency was found through NCSs.

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