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- W3150222868 abstract "The Artificial Intelligence field is under the spotlight as of its wide use and efficiency in solving real world problems. As of this decade, a notable rise in the amounts of data collected, which were made available to the public, is witnessed. This allowed the emergence of many research problems among which working with short texts and their different challenges. In this paper, we propose the collapsed Gibbs Sampling algorithm for the generalized Dirichlet Multinomial Mixture model for short text clustering (GSDMM). The proposed approach has been evaluated on the Google News dataset. Our approach proved to be more efficient than the related-works and succeeded into overcoming the common challenges that come with short texts." @default.
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- W3150222868 date "2021-01-01" @default.
- W3150222868 modified "2023-09-26" @default.
- W3150222868 title "Short Text Clustering Using Generalized Dirichlet Multinomial Mixture Model" @default.
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- W3150222868 doi "https://doi.org/10.1007/978-981-16-1685-3_13" @default.
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