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Other literature type . 2023
License: CC BY
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Presentation . 2023
License: CC BY
Data sources: Datacite
ZENODO
Presentation . 2023
License: CC BY
Data sources: Datacite
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Development of an automatic text-based (inter)disciplinary classification of projects and publications using NLP

Authors: Florian Kotschka; Rolf Guescini; Alshawaf, Fadwa;

Development of an automatic text-based (inter)disciplinary classification of projects and publications using NLP

Abstract

The approach of analyzing and classifying research projects and outputs using texts available on websites as well as the collection of archived research papers can play an important role in structuring and presenting research information. Natural language processing (NLP) and OpenAI has the potential of significantly reducing the effort and time with the growing need of documenting research information. We have been working on the subject of classifying research papers and projects within the frame work of the BUA-VIVO-Project. Florian Kotschka and Rolf Guescini from the VIVO Team would present and discuss the achieved results with the community. Here is the Link to the Video and the project website.

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Keywords

Document Classification, NLP, Ontologies, VIVO

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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