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Semi-automatic Construction of Learning Set and Integration of Automatic Classification for Academic Literature in Technical Sciences

  • Journal of the Korean Society for Information Management
  • Abbr : JKOSIM
  • 2018, 35(4), pp.141~164
  • DOI : 10.3743/KOSIM.2018.35.4.141
  • Publisher : 한국정보관리학회
  • Research Area : Interdisciplinary Studies > Library and Information Science
  • Received : November 17, 2018
  • Accepted : December 17, 2018
  • Published : December 30, 2018

Seon-Wu Kim 1 Gun-Woo Ko 1 Won-Jun Choi 2 Hee-Seok Jeong 2 Hwa-Mook Yoon 3 Sung-Pil Choi 4

1경기대학교 문헌정보학과
2한국과학기술정보연구원 콘텐츠 큐레이션센터
3한국과학기술정보연구원 콘텐츠큐레이션센터
4경기대학교

Accredited

ABSTRACT

Recently, as the amount of academic literature has increased rapidly and complex researches have been actively conducted, researchers have difficulty in analyzing trends in previous research. In order to solve this problem, it is necessary to classify information in units of academic papers. However, in Korea, there is no academic database in which such information is provided. In this paper, we propose an automatic classification system that can classify domestic academic literature into multiple classes. To this end, first, academic documents in the technical science field described in Korean were collected and mapped according to class 600 of the DDC by using K-Means clustering technique to construct a learning set capable of multiple classification. As a result of the construction of the training set, 63,915 documents in the Korean technical science field were established except for the values ​​in which metadata does not exist. Using this training set, we implemented and learned the automatic classification engine of academic documents based on deep learning. Experimental results obtained by hand-built experimental set-up showed 78.32% accuracy and 72.45% F1 performance for multiple classification.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.