Year: 2024
Type: article
Abstract: Topic modeling is a commonly used text-mining tool for discovering hidden semantic structures within a text corpus. This paper introduces an unsupervised learning-based topic modeling approach for Urdu documents, a language with limited resources. By leveraging unsupervised learning techniques such as Latent Dirichlet Allocation (LDA) and Unsupervised Latent Semantic Indexing (ULSI), specific and accurate topics are extracted from Urdu texts. The experimental results illustrate the dominance of our recommended ULSI model and LDA model, achieving 99% and 98% accuracy, and 44% and 37% coherence values in LDA and ULSI, respectively. The experimental results demonstrate the superiority of the proposed ULSI and LDA models, which achieve high accuracy and coherence values.
Authors: Muhammad Ashir, Ali Saeed, Muhammad Farhat Ullah, Syed Nasir Ali, Muhammad Sauood, Mehmood Anwar, Naveed Hussain
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4406053058