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 -