http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
머신러닝 기법을 이용한 한국어 보이스피싱 텍스트 분류 성능 분석
무사부부수구밀란두키스 ( Milandu Keith Moussavou Boussougou ),진상윤 ( Sangyoon Jin ),장대호 ( Daeho Chang ),박동주 ( Dong-joo Park ) 한국정보처리학회 2021 한국정보처리학회 학술대회논문집 Vol.28 No.2
Text classification is one of the popular tasks in Natural Language Processing (NLP) used to classify text or document applications such as sentiment analysis and email filtering. Nowadays, state-of-the-art (SOTA) Machine Learning (ML) and Deep Learning (DL) algorithms are the core engine used to perform these classification tasks with high accuracy, and they show satisfying results. This paper conducts a benchmarking performance’s analysis of multiple SOTA algorithms on the first known labeled Korean voice phishing dataset called KorCCVi. Experimental results reveal performed on a test set of 366 samples reveal which algorithm performs the best considering the training time and metrics such as accuracy and F1 score.