InforLorV4, Main, Exploration, bibRecord, 001622

Combining Neural Clustering with Intelligent Labeling and Unsupervised Bayesian Reasoning in a Multiview Context for Efficient Diachronic Analysis

Identifieur interne : 001622 ( Main/Exploration ); précédent : 001621; suivant : 001623

Combining Neural Clustering with Intelligent Labeling and Unsupervised Bayesian Reasoning in a Multiview Context for Efficient Diachronic Analysis

Auteurs : Jean-Charles Lamirel [France]

Source :

Advances in Intelligent Systems and Computing [ 2194-5357 ]

RBID : ISTEX:B586661D5E9682753AF2ACEDBEF63572431C0AAF

Abstract

Abstract: To cope with the current defects of existing incremental clustering methods, an alternative approach for accurately analyzing textual information evolving over time consists in performing diachronic analysis. This type of analysis is based on the application of a clustering method on data associated with two, or more, successive periods of time, and on the study of the evolution of the clusters contents and of their mappings between the different periods. This paper propose a new unsupervised approach for dealing with time evolving information with is based on the combination of neural clustering and unsupervised Bayesian reasoning. The experimental context is related to the study of the evolution of research fields in scientific literature.

Url:

https://api.istex.fr/ark:/67375/HCB-MGR2B3TD-6/fulltext.pdf

DOI: 10.1007/978-3-642-35230-0_25

Affiliations:

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Le document en format XML

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<front><div type="abstract" xml:lang="en">Abstract: To cope with the current defects of existing incremental clustering methods, an alternative approach for accurately analyzing textual information evolving over time consists in performing diachronic analysis. This type of analysis is based on the application of a clustering method on data associated with two, or more, successive periods of time, and on the study of the evolution of the clusters contents and of their mappings between the different periods. This paper propose a new unsupervised approach for dealing with time evolving information with is based on the combination of neural clustering and unsupervised Bayesian reasoning. The experimental context is related to the study of the evolution of research fields in scientific literature.</div>
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Combining Neural Clustering with Intelligent Labeling and Unsupervised Bayesian Reasoning in a Multiview Context for Efficient Diachronic Analysis

Combining Neural Clustering with Intelligent Labeling and Unsupervised Bayesian Reasoning in a Multiview Context for Efficient Diachronic Analysis

Source :

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri