계층적 표현 및 레이블 임베딩을 활용한 국내 논문 문장 의미 분류 모델 = Hierarchical Representation and Label Embedding for Semantic Classification of Domestic Research Paper

다국어 초록 (Multilingual Abstract)

The sentence's meaning in the paper is that it has a hierarchical structure, and there is data imbalance among subcategories. In addition, the meaning of the sentence in the paper is closely related to its position within the paper. Existing flat classification methods mainly consider only subcategories, leading to a decrease in classification accuracy due to data imbalance. In response to this, this study proposes hierarchical representation and label embedding methods to perform hierarchical semantic classification of sentences effectively. In addition, the section names of the paper are actively utilized to represent the positional information of the paper sentences. Through experiments, it is demonstrated that the proposed method, which explicitly considers hierarchical and positional information in the KISTI domestic paper sentence semantic tagging dataset, achieves excellent performance in terms of F1 score.