Keyword Extraction in Economics Literatures using Natural Language Processing

Soojeong Kim; Sunho Choi; Junhee Seok

doi:10.1109/ICUFN49451.2021.9528546

Keyword Extraction in Economics Literatures using Natural Language Processing

Soojeong Kim, Sunho Choi, Junhee Seok

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (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 language	English
Title of host publication	ICUFN 2021 - 2021 12th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	75-77
Number of pages	3
ISBN (Electronic)	9781728164762
DOIs	https://doi.org/10.1109/ICUFN49451.2021.9528546
Publication status	Published - 2021 Aug 17
Event	12th International Conference on Ubiquitous and Future Networks, ICUFN 2021 - Virtual, Jeju Island, Korea, Republic of Duration: 2021 Aug 17 → 2021 Aug 20

Publication series

Name	International Conference on Ubiquitous and Future Networks, ICUFN
Volume	2021-August
ISSN (Print)	2165-8528
ISSN (Electronic)	2165-8536

Conference

Conference	12th International Conference on Ubiquitous and Future Networks, ICUFN 2021
Country/Territory	Korea, Republic of
City	Virtual, Jeju Island
Period	21/8/17 → 21/8/20

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea grant (NRF-2019R1A2C1084778). Correspondence should be addressed to jseok14@korea.ac.kr

Publisher Copyright:
© 2021 IEEE.

Keywords

BERT
Economics Journal Paper
Natural Language Processing
Preprocessing

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture

Access to Document

10.1109/ICUFN49451.2021.9528546

Cite this

Kim, S., Choi, S., & Seok, J. (2021). Keyword Extraction in Economics Literatures using Natural Language Processing. In ICUFN 2021 - 2021 12th International Conference on Ubiquitous and Future Networks (pp. 75-77). (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2021-August). IEEE Computer Society. https://doi.org/10.1109/ICUFN49451.2021.9528546

Keyword Extraction in Economics Literatures using Natural Language Processing. / Kim, Soojeong; Choi, Sunho; Seok, Junhee.
ICUFN 2021 - 2021 12th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2021. p. 75-77 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2021-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, S, Choi, S & Seok, J 2021, Keyword Extraction in Economics Literatures using Natural Language Processing. in ICUFN 2021 - 2021 12th International Conference on Ubiquitous and Future Networks. International Conference on Ubiquitous and Future Networks, ICUFN, vol. 2021-August, IEEE Computer Society, pp. 75-77, 12th International Conference on Ubiquitous and Future Networks, ICUFN 2021, Virtual, Jeju Island, Korea, Republic of, 21/8/17. https://doi.org/10.1109/ICUFN49451.2021.9528546

@inproceedings{93072f1ecdc74d50967beb2989c8f72c,

title = "Keyword Extraction in Economics Literatures using Natural Language Processing",

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. ",

keywords = "BERT, Economics Journal Paper, Natural Language Processing, Preprocessing",

author = "Soojeong Kim and Sunho Choi and Junhee Seok",

note = "Funding Information: This work was supported by the National Research Foundation of Korea grant (NRF-2019R1A2C1084778). Correspondence should be addressed to jseok14@korea.ac.kr Publisher Copyright: {\textcopyright} 2021 IEEE.; 12th International Conference on Ubiquitous and Future Networks, ICUFN 2021 ; Conference date: 17-08-2021 Through 20-08-2021",

year = "2021",

month = aug,

day = "17",

doi = "10.1109/ICUFN49451.2021.9528546",

language = "English",

series = "International Conference on Ubiquitous and Future Networks, ICUFN",

publisher = "IEEE Computer Society",

pages = "75--77",

booktitle = "ICUFN 2021 - 2021 12th International Conference on Ubiquitous and Future Networks",

}

TY - GEN

T1 - Keyword Extraction in Economics Literatures using Natural Language Processing

AU - Kim, Soojeong

AU - Choi, Sunho

AU - Seok, Junhee

N1 - Funding Information: This work was supported by the National Research Foundation of Korea grant (NRF-2019R1A2C1084778). Correspondence should be addressed to jseok14@korea.ac.kr Publisher Copyright: © 2021 IEEE.

PY - 2021/8/17

Y1 - 2021/8/17

N2 - 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.

AB - 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.

KW - BERT

KW - Economics Journal Paper

KW - Natural Language Processing

KW - Preprocessing

UR - http://www.scopus.com/inward/record.url?scp=85115615238&partnerID=8YFLogxK

U2 - 10.1109/ICUFN49451.2021.9528546

DO - 10.1109/ICUFN49451.2021.9528546

M3 - Conference contribution

AN - SCOPUS:85115615238

T3 - International Conference on Ubiquitous and Future Networks, ICUFN

SP - 75

EP - 77

BT - ICUFN 2021 - 2021 12th International Conference on Ubiquitous and Future Networks

PB - IEEE Computer Society

T2 - 12th International Conference on Ubiquitous and Future Networks, ICUFN 2021

Y2 - 17 August 2021 through 20 August 2021

ER -

Keyword Extraction in Economics Literatures using Natural Language Processing

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this