This study investigates the application of Fuzzy Latent Semantic Analysis (fLSA) in analyzing expenditure chapters within legal texts, using Italy’s budget law 178/2020 as a case study. Faced with challenges in legal studies, such as specialized language and heterogeneity, fLSA combines Latent Semantic Analysis (LSA) with dimensionality reduction and soft clustering. Results show comparable performance with the widely used Latent Dirichlet Allocation (LDA) in identifying coherent expenditure chapters, with fLSA showing a tendency to retrieve more distinctive and exclusive topics.
A Fuzzy Topic Modeling Approach to legal corpora
Antonio Calcagni
;Arjuna Tuzzi
2024
Abstract
This study investigates the application of Fuzzy Latent Semantic Analysis (fLSA) in analyzing expenditure chapters within legal texts, using Italy’s budget law 178/2020 as a case study. Faced with challenges in legal studies, such as specialized language and heterogeneity, fLSA combines Latent Semantic Analysis (LSA) with dimensionality reduction and soft clustering. Results show comparable performance with the widely used Latent Dirichlet Allocation (LDA) in identifying coherent expenditure chapters, with fLSA showing a tendency to retrieve more distinctive and exclusive topics.
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