상세정보
학술대회
Improving Text Classification Performance through Data Labeling Adjustment
- 발행일
- 202210
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.2277-2279
- 협약과제
- 22JR2200, 119신고 접수에 대한 딥러닝 기반 재난 상황인지 및 대응지원 모델링 기술 개발, 권은정
- 초록
- The data labeling process in natural language processing (NLP) technology is an essential process for identifying raw text data and adding information labels to provide context for machines to learn. However, labeling set of data is one of the most time-consuming parts of any machine learning application, and requires to verify the accuracy of labels and adjusts them correctly as necessary. In this paper, we propose a method to adjust data labels to better fit the meaning of sentences to improve model performance. In addition, we show that our approach is feasible to improve model performance through various experiments
- KSP 제안 키워드
- Classification Performance, Data Labeling, Model performance, Natural Language Processing, Verify the accuracy, learning application, machine Learning, text classification, text data