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Comparison of Two-pass Algorithms for Dynamic Topic Modelling Based on Matrix Decompositions

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Název: Comparison of Two-pass Algorithms for Dynamic Topic Modelling Based on Matrix Decompositions
Autoři: Cardiff, John, Skitalinskaya, Gabriella, Alexandrov, Mikhail
Zdroj: Conference Papers
Informace o vydavateli: Technological University Dublin
Rok vydání: 2017
Sbírka: Dublin Institute of Technology: ARROW@DIT (Archiving Research Resources on he Web)
Témata: Dynamic Topic Modeling, Matrix Decomposition, Latent Dirichlet Allocation, Computer Sciences, Numerical Analysis and Scientific Computing
Popis: In this paper we present a two-pass algorithm based on different matrix decompositions, such as LSI, PCA, ICA and NMF, which allows tracking of the evolution of topics over time. The proposed dynamic topic models as output give an easily interpreted overview of topics found in a sequentially organized set of documents that does not require further processing. Each topic is presented by a user-specified number of top-terms. Such an approach to topic modeling if applied to, for example, a news article data set, can be convenient and useful for economists, sociologists, political scientists. The proposed approach allows to achieve results comparable to those obtained using complex probabilistic models, such as LDA.
Druh dokumentu: conference object
Popis souboru: application/pdf
Jazyk: unknown
Relation: https://arrow.tudublin.ie/ittscicon/11; https://arrow.tudublin.ie/context/ittscicon/article/1011/viewcontent/2017_Skitalinskaya_et_al_Comparison_of_two_pass_algorithms.pdf
DOI: 10.1007/978-3-030-02840-4_3
Dostupnost: https://arrow.tudublin.ie/ittscicon/11
https://doi.org/10.1007/978-3-030-02840-4_3
https://arrow.tudublin.ie/context/ittscicon/article/1011/viewcontent/2017_Skitalinskaya_et_al_Comparison_of_two_pass_algorithms.pdf
Rights: Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence ; http://creativecommons.org/licenses/by/4.0/
Přístupové číslo: edsbas.7ACE3C23
Databáze: BASE
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  Data: Comparison of Two-pass Algorithms for Dynamic Topic Modelling Based on Matrix Decompositions
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  Data: <searchLink fieldCode="AR" term="%22Cardiff%2C+John%22">Cardiff, John</searchLink><br /><searchLink fieldCode="AR" term="%22Skitalinskaya%2C+Gabriella%22">Skitalinskaya, Gabriella</searchLink><br /><searchLink fieldCode="AR" term="%22Alexandrov%2C+Mikhail%22">Alexandrov, Mikhail</searchLink>
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  Data: In this paper we present a two-pass algorithm based on different matrix decompositions, such as LSI, PCA, ICA and NMF, which allows tracking of the evolution of topics over time. The proposed dynamic topic models as output give an easily interpreted overview of topics found in a sequentially organized set of documents that does not require further processing. Each topic is presented by a user-specified number of top-terms. Such an approach to topic modeling if applied to, for example, a news article data set, can be convenient and useful for economists, sociologists, political scientists. The proposed approach allows to achieve results comparable to those obtained using complex probabilistic models, such as LDA.
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