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Geoinference of author affiliations using NLP-based text classification

Geoinference of author affiliations using NLP-based text classification

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_7a8f6b28c993440a909bfe4d900db0f8

Geoinference of author affiliations using NLP-based text classification

About this item

Full title

Geoinference of author affiliations using NLP-based text classification

Publisher

London: Nature Publishing Group UK

Journal title

Scientific reports, 2024-10, Vol.14 (1), p.24306-8, Article 24306

Language

English

Formats

Publication information

Publisher

London: Nature Publishing Group UK

More information

Scope and Contents

Contents

Author affiliations are essential in bibliometric studies, requiring relevant information extraction from free-text affiliations. Precisely determining an author’s location from their affiliation is crucial for understanding research networks, collaborations, and geographic distribution. Existing geoparsing tools using regular expressions have limi...

Alternative Titles

Full title

Geoinference of author affiliations using NLP-based text classification

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_7a8f6b28c993440a909bfe4d900db0f8

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_7a8f6b28c993440a909bfe4d900db0f8

Other Identifiers

ISSN

2045-2322

E-ISSN

2045-2322

DOI

10.1038/s41598-024-73318-7

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