Topic Modeling Techniques for Document Clustering and Analysis of Judicial Judgements
Topic Modeling Techniques for Document Clustering and Analysis of Judicial Judgements
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Amar Jeet Rawat, Sunil Ghildiyal, Anil Kumar Dixit
|DOI : 10.14445/22315381/IJETT-V70I11P217|
How to Cite?
Amar Jeet Rawat, Sunil Ghildiyal, Anil Kumar Dixit, "Topic Modeling Techniques for Document Clustering and Analysis of Judicial Judgements," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 163-169, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P217
The digital world is growing rapidly in every dimension. Legal case information and judgements are now available online and are becoming a big problem because of their unstructured textual nature. The classification, analysis, and understanding of such unstructured textual data are complex. Various top-modelling techniques are used for the classification of such corpora. In this paper, two popular topic-modelling models, LDA and LSA, are implemented, and their performances are compared on a dataset of 1000 legal judgement documents. Coherence scores are used to evaluate the performance of both models. Tests show that LDA and LSA have different areas of strength. LDA is good at learning about descriptive topics, while LSA is good at making a short representation of the meaning of documents and words in a corpus.
Latent Semantic Analysis, Topic Modeling, Natural Language Processing, Document Clustering, Latent Dirichlet Allocation.
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