Keyword Extraction in Economics Literatures using Natural Language Processing

Soojeong Kim, Sunho Choi, Junhee Seok

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Using Natural Language Process (NLP) as an efficient way to research paper is important when user feedback is sparse or unavailable. The task of text mining research paper is challenging, mainly due to the problem of unique characteristics such as jargon. Nowadays, there exist many language models that learn deep semantic representations by being trained on huge corpora. In this paper, we specify the NLP pre-processing process with Economics journal paper and apply it to a deep learning model to extract keywords. Here, we focus on the strength of NLP when applied to an unknown field. The analysis result shows the possibility and potential usefulness of the relationship research between keywords in research papers.

Original languageEnglish
Title of host publicationICUFN 2021 - 2021 12th International Conference on Ubiquitous and Future Networks
PublisherIEEE Computer Society
Pages75-77
Number of pages3
ISBN (Electronic)9781728164762
DOIs
Publication statusPublished - 2021 Aug 17
Event12th International Conference on Ubiquitous and Future Networks, ICUFN 2021 - Virtual, Jeju Island, Korea, Republic of
Duration: 2021 Aug 172021 Aug 20

Publication series

NameInternational Conference on Ubiquitous and Future Networks, ICUFN
Volume2021-August
ISSN (Print)2165-8528
ISSN (Electronic)2165-8536

Conference

Conference12th International Conference on Ubiquitous and Future Networks, ICUFN 2021
Country/TerritoryKorea, Republic of
CityVirtual, Jeju Island
Period21/8/1721/8/20

Keywords

  • BERT
  • Economics Journal Paper
  • Natural Language Processing
  • Preprocessing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

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