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자질선정을 통한 국내 학술지 논문의 자동분류에 관한 연구
An Experimental Study on the Automatic Classification of Korean Journal Articles through Feature Selection 원문보기

정보관리학회지 = Journal of the Korean society for information management, v.39 no.1, 2022년, pp.69 - 90  

김판준 (신라대학교 문헌정보학과)

초록
AI-Helper 아이콘AI-Helper

국내 학술연구의 동향을 구체적으로 파악하여 연구개발 활동의 체계적인 지원 및 평가는 물론 현재와 미래의 연구 방향을 설정할 수 있는 기초 데이터로서, 개별 학술지 논문에 표준화된 주제 범주(통제키워드)를 부여할 수 있는 효율적인 방안을 모색하였다. 이를 위해 한국연구재단 「학술연구분야분류표」 상의 분류 범주를 국내학술지 논문에 자동 할당하는 과정에서, 자질선정 기법을 중심으로 자동분류의 성능에 영향을 미치는 주요 요소들에 대한 다각적인 실험을 수행하였다. 그 결과, 실제 환경의 불균형 데이터세트(imbalanced dataset)인 국내 학술지 논문의 자동분류에서는 보다 단순한 분류기와 자질선정 기법, 그리고 비교적 소규모의 학습집합을 사용하여 상당히 좋은 수준의 성능을 기대할 수 있는 것으로 나타났다.

Abstract ▼ AI-Helper 아이콘AI-Helper

As basic data that can systematically support and evaluate R&D activities as well as set current and future research directions by grasping specific trends in domestic academic research, I sought efficient ways to assign standardized subject categories (control keywords) to individual journal papers...

주제어

#자동분류   #텍스트 범주화   #자질선정   #필터   #학술지 논문  

표/그림 (18)

참고문헌 (49)

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