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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

Short Text Clustering Using Generalized Dirichlet Multinomial Mixture Model

verfasst von : Samar Hannachi, Fatma Najar, Nizar Bouguila

Erschienen in: Recent Challenges in Intelligent Information and Database Systems

Verlag: Springer Singapore

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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.

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Vorheriges Kapitel Hybrid Approach for the Semantic Analysis of Texts in the Kazakh Language
Nächstes Kapitel Comparative Study of Machine Learning Algorithms for Performant Text Analysis in a Real World System
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Metadaten
Titel
Short Text Clustering Using Generalized Dirichlet Multinomial Mixture Model
verfasst von
Samar Hannachi
Fatma Najar
Nizar Bouguila
Copyright-Jahr
2021
Verlag
Springer Singapore
Buch
Recent Challenges in Intelligent Information and Database Systems

Print ISBN: 978-981-16-1684-6

Electronic ISBN: 978-981-16-1685-3

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-981-16-1685-3_13