Topic modeling by latent dirichlet allocation for Thai language?
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Topic modeling using Latent Dirichlet Allocation (LDA) has been explored for the Thai language . The complexity of the Thai language, with its lack of word delimiters and composite words, poses challenges for text mining and natural language processing tasks . Previous research has focused on applying refined stop-word lists and N-gram techniques to improve topic modeling in Thai . This paper is the first to compare LDA with Nonnegative Matrix Factorization (NMF) for topic modeling in Thai and evaluate their coherence using state-of-the-art metrics . The findings of this study contribute to understanding the effectiveness of different topic modeling approaches in the Thai language .
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58 Citations | The paper does not specifically mention topic modeling using Latent Dirichlet Allocation for the Thai language. |
| The paper does not mention anything about topic modeling using latent Dirichlet allocation specifically for the Thai language. | |
26 May 2022 1 Citations | The paper does not specifically mention topic modeling by Latent Dirichlet Allocation for the Thai language. |
24 Jun 2020 | The paper explores the use of Latent Dirichlet Allocation (LDA) and Nonnegative Matrix Factorization (NMF) for topic modeling in Thai language and compares their coherence. |
| The provided paper does not specifically mention topic modeling by Latent Dirichlet Allocation for the Thai language. |
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