- P-ISSN1013-0799
- E-ISSN2586-2073
- KCI
기계학습을 통한 디스크립터 자동부여에 관한 연구
A Study on automatic assignment of descriptors using machine learning
초록
학술지 논문에 디스크립터를 자동부여하기 위하여 기계학습 기반의 접근법을 적용하였다. 정보학 분야의 핵심 학술지를 선정하여 지난 11년간 수록된 논문들을 대상으로 문헌집단을 구성하였고, 자질 선정과 학습집합의 크기에 따른 성능을 살펴보았다. 자질 선정에서는 카이제곱 통계량(CHI)과 고빈도 선호 자질 선정 기준들(COS, GSS, JAC)을 사용하여 자질을 축소한 다음, 지지벡터기계(SVM)로 학습한 결과가 가장 좋은 성능을 보였다. 학습집합의 크기에서는 지지벡터기계(SVM)와 투표형 퍼셉트론(VPT)의 경우에는 상당한 영향을 받지만 나이브 베이즈(NB)의 경우에는 거의 영향을 받지 않는 것으로 나타났다.
- keywords
- descriptors, automatic indexing, machine learning, feature selection, classifier, text categorization, descriptors, automatic indexing, machine learning, feature selection, classifier, text categorization, 디스크립터, 자동색인, 기계학습, 자질 선정, 분류기, 텍스트 범주화
Abstract
This study utilizes various approaches of machine learning in the process of automatically assigning descriptors to journal articles. After selecting core journals in the field of information science and organizing test collection from the articles of the past 11 years, the effectiveness of feature selection and the size of training set was examined. In the regard of feature selection, after reducing the feature set by χ2 statistics(CHI) and criteria which prefer high-frequency features(COS, GSS, JAC), the trained Support Vector Machines(SVM) performs the best. With respective to the size of the training set, it significantly influences the performance of Support Vector Machines(SVM) and Voted Perceptron(VTP). but it scarcely affects that of Naive Bayes(NB).
- keywords
- descriptors, automatic indexing, machine learning, feature selection, classifier, text categorization, descriptors, automatic indexing, machine learning, feature selection, classifier, text categorization, 디스크립터, 자동색인, 기계학습, 자질 선정, 분류기, 텍스트 범주화
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