Leveraging Natural Language Processing to Analyze Scientific Content: Proposal of an NLP Pipeline for the Field of Computer Vision

Henrik Kortum (), Max Leimkühler () and Oliver Thomas
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Henrik Kortum: German Research Center for Artificial Intelligence
Max Leimkühler: German Research Center for Artificial Intelligence
Oliver Thomas: German Research Center for Artificial Intelligence

A chapter in Innovation Through Information Systems, 2021, pp 40-55 from Springer

Abstract: Abstract In this paper we elaborate the opportunity of using natural language processing to analyze scientific content both, from a practical as well as a theoretical point of view. Firstly, we conducted a literature review to summarize the status quo of using natural language processing for analyzing scientific content. We could identify different approaches, e.g., with the aim of clustering and tagging publications or to summarize scientific papers. Secondly, we conducted a case study where we used our proposed natural language processing pipeline to analyze scientific content about computer vision available at the database IEEE. Our method helped us to identify emerging trends in the recent years and give an overview of the field of research.

Keywords: Natural language processing; Machine learning; Emerging trends; Computer vision; W2V (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-86797-3_3

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DOI: 10.1007/978-3-030-86797-3_3

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