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A Gamma-Poisson Mixture Topic Model for Short Text
Joint Authors
Mazarura, Jocelyn
de Waal, Alta
de Villiers, Pieter
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-29
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Most topic models are constructed under the assumption that documents follow a multinomial distribution.
The Poisson distribution is an alternative distribution to describe the probability of count data.
For topic modelling, the Poisson distribution describes the number of occurrences of a word in documents of fixed length.
The Poisson distribution has been successfully applied in text classification, but its application to topic modelling is not well documented, specifically in the context of a generative probabilistic model.
Furthermore, the few Poisson topic models in the literature are admixture models, making the assumption that a document is generated from a mixture of topics.
In this study, we focus on short text.
Many studies have shown that the simpler assumption of a mixture model fits short text better.
With mixture models, as opposed to admixture models, the generative assumption is that a document is generated from a single topic.
One topic model, which makes this one-topic-per-document assumption, is the Dirichlet-multinomial mixture model.
The main contributions of this work are a new Gamma-Poisson mixture model, as well as a collapsed Gibbs sampler for the model.
The benefit of the collapsed Gibbs sampler derivation is that the model is able to automatically select the number of topics contained in the corpus.
The results show that the Gamma-Poisson mixture model performs better than the Dirichlet-multinomial mixture model at selecting the number of topics in labelled corpora.
Furthermore, the Gamma-Poisson mixture produces better topic coherence scores than the Dirichlet-multinomial mixture model, thus making it a viable option for the challenging task of topic modelling of short text.
American Psychological Association (APA)
Mazarura, Jocelyn& de Waal, Alta& de Villiers, Pieter. 2020. A Gamma-Poisson Mixture Topic Model for Short Text. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1195381
Modern Language Association (MLA)
Mazarura, Jocelyn…[et al.]. A Gamma-Poisson Mixture Topic Model for Short Text. Mathematical Problems in Engineering No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1195381
American Medical Association (AMA)
Mazarura, Jocelyn& de Waal, Alta& de Villiers, Pieter. A Gamma-Poisson Mixture Topic Model for Short Text. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1195381
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195381