Introductory Chapter: Development of Data Clustering

Tang, Niansheng; Wu, Ying

doi:10.5772/intechopen.104505

Cited by 1 publication

(1 citation statement)

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“…www.ijacsa.thesai.org embeddings, which are texts represented in a semantic vector space [9]. Cosine similarity is another method that uses unsupervised learning techniques like Word2Vec CBoW, Word2Vec Skip-gram, and TF-IDF to determine how similar court documents are to one another [10]. Cosine similarity is employed to group lawful [11].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Cosine Similarity Algorithm on Omnibus Law Drafting

Syam,

et al. 2024

IJACSA

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Drafting of Omnibus Laws presents a complex challenge in legal governance, often involving the integration and consolidation of disparate legal provisions into a unified framework. In this context, the application of advanced computational techniques becomes crucial for streamlining the drafting process and ensuring coherence across the law's various components. Cosine similarity, a widely used measure in natural language processing and document analysis, offers a quantitative means to assess the similarity between different sections or articles within the Omnibus Law draft. By representing legal texts as high-dimensional vectors in a vector space model, cosine similarity enables the comparison of textual similarity based on the cosine of the angle between these vectors. Implementing cosine similarity in the context of omnibus law using FastAPI and Laravel can be a valuable tool for analyzing similarity between legal documents, especially in the context of omnibus law. Legal practitioners and researchers can use the cosine similarity measure to compare the textual content of different legal documents and identify similarities. This can aid in tasks such as legal document retrieval, clustering similar provisions, and detecting potential inconsistencies. The combination of FastAPI and Laravel provides a potent and efficient way to develop and deploy this functionality, contributing to the advancement of legal informatics and analysis. The dataset used is Undang-Undang (UU) which used Bahasa from 1945 to 2022, comprising a total of 1705 UU. The implemented cosine similarity yielded a recall rate of 90.10% on the law.

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Section: Introductionmentioning

confidence: 99%

Implementation of Cosine Similarity Algorithm on Omnibus Law Drafting

Syam,

et al. 2024

IJACSA

View full text Add to dashboard Cite

show abstract

Introductory Chapter: Development of Data Clustering

Cited by 1 publication

References 17 publications

Implementation of Cosine Similarity Algorithm on Omnibus Law Drafting

Implementation of Cosine Similarity Algorithm on Omnibus Law Drafting

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