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Chronological corpora curve clustering: From scientific corpora construction to knowledge dynamics discovery through word life-cycles clustering

Aim of this procedural method is to construct well-founded corpora of scientific literature, and, hence, to track the evolution of knowledge fields from the reconstruction and clustering of words’ life-cycles. The method contains: • an original selection process of relevant keywords involving the id...

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Detalles Bibliográficos
Autores principales: Trevisani, Matilde, Tuzzi, Arjuna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287063/
https://www.ncbi.nlm.nih.gov/pubmed/30568881
http://dx.doi.org/10.1016/j.mex.2018.11.010
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author Trevisani, Matilde
Tuzzi, Arjuna
author_facet Trevisani, Matilde
Tuzzi, Arjuna
author_sort Trevisani, Matilde
collection PubMed
description Aim of this procedural method is to construct well-founded corpora of scientific literature, and, hence, to track the evolution of knowledge fields from the reconstruction and clustering of words’ life-cycles. The method contains: • an original selection process of relevant keywords involving the identification of relevant stems and stem n-grams through a matching with item lists of relevant glossaries; • several types of normalization of temporal trajectories of word raw frequencies; • a properly customized clustering of word life-cycles, with a graphical extensive investigation of the best candidates for cluster number, to unveil the important dynamics and decipher the history of a scientific field.
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spelling pubmed-62870632018-12-19 Chronological corpora curve clustering: From scientific corpora construction to knowledge dynamics discovery through word life-cycles clustering Trevisani, Matilde Tuzzi, Arjuna MethodsX Computer Science Aim of this procedural method is to construct well-founded corpora of scientific literature, and, hence, to track the evolution of knowledge fields from the reconstruction and clustering of words’ life-cycles. The method contains: • an original selection process of relevant keywords involving the identification of relevant stems and stem n-grams through a matching with item lists of relevant glossaries; • several types of normalization of temporal trajectories of word raw frequencies; • a properly customized clustering of word life-cycles, with a graphical extensive investigation of the best candidates for cluster number, to unveil the important dynamics and decipher the history of a scientific field. Elsevier 2018-11-19 /pmc/articles/PMC6287063/ /pubmed/30568881 http://dx.doi.org/10.1016/j.mex.2018.11.010 Text en © 2018 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Computer Science
Trevisani, Matilde
Tuzzi, Arjuna
Chronological corpora curve clustering: From scientific corpora construction to knowledge dynamics discovery through word life-cycles clustering
title Chronological corpora curve clustering: From scientific corpora construction to knowledge dynamics discovery through word life-cycles clustering
title_full Chronological corpora curve clustering: From scientific corpora construction to knowledge dynamics discovery through word life-cycles clustering
title_fullStr Chronological corpora curve clustering: From scientific corpora construction to knowledge dynamics discovery through word life-cycles clustering
title_full_unstemmed Chronological corpora curve clustering: From scientific corpora construction to knowledge dynamics discovery through word life-cycles clustering
title_short Chronological corpora curve clustering: From scientific corpora construction to knowledge dynamics discovery through word life-cycles clustering
title_sort chronological corpora curve clustering: from scientific corpora construction to knowledge dynamics discovery through word life-cycles clustering
topic Computer Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287063/
https://www.ncbi.nlm.nih.gov/pubmed/30568881
http://dx.doi.org/10.1016/j.mex.2018.11.010
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