Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 4th International Conference on Signal Processing and Machine Learning

Series Vol. 52 , 27 March 2024


Open Access | Article

Performance evaluation of Latent Dirichlet Allocation on legal documents

A. O. Ogundare * 1 , A. U. Saleh 2 , O. A. James 3 , E. E. Ajayi 4 , S. Gostojić 5
1 University of Novi Sad
2 Istanbul Ticaret University
3 Federal University of Technology Owerri
4 University of Belgrade
5 University of Novi Sad

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 52, 96-101
Published 27 March 2024. © 27 March 2024 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation A. O. Ogundare, A. U. Saleh, O. A. James, E. E. Ajayi, S. Gostojić. Performance evaluation of Latent Dirichlet Allocation on legal documents. ACE (2024) Vol. 52: 96-101. DOI: 10.54254/2755-2721/52/20241322.

Abstract

Latent Dirichlet Allocation (LDA) is an algorithm with the capability of processing large amount of text data. In this study, the LDA is used to produce topic modelling of topic clusters from corpus of legal texts generated under 4 topics within Nigeria context– Employment Contract, Election Petition, Deeds, and Articles of Incorporation. Each topic has a substantial number of articles and the LDA method proves effective in extracting topics and generating index words that are in each topic cluster. At the end of experimentation, results are compared with manually pre-annotated dataset for validation purpose and the results show high accuracy. The LDA output shows optimal performance in the word indexing processing for Election Petition as all the documents annotated under the topic were accurately classified.

Keywords

Latent Dirichlet Allocation, Probabilistic Latent Semantic Indexing, Latent Semantic Indexing

References

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4. Edi, S. N and A. Ria. "A Review on Overlapping and Non-Overlapping Community Detection Algorithms for Social Network Analytics ." Far East Journal of Electronics and Communications (2018): 1-27.

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13. Ogundare, D. (2023). Topic Modelling Legal Documents (v1.0.0). GitHub. https://github.com/dotun-ogundare/topic-modelling-legal-documents

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 4th International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-83558-349-4
ISBN (Online)
978-1-83558-350-0
Published Date
27 March 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/52/20241322
Copyright
27 March 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated