Youngjoong Ko
성균관대학교 공과대학 자연어처리연구실 · 컴퓨터과학
Youngjoong Ko 교수의 연구실은 텍스트 분류, 정보 검색 및 다국어 정보 처리 분야에서 주로 활동하고 있습니다. 특히 레이블이 없는 데이터에서 자동으로 카테고리를 학습하는 비지도 학습 기반 텍스트 분류 기법과, 문장의 중요도를 고려한 특성 가중치 설정 기법을 중심으로 연구를 진행하고 있습니다. 또한, 번역 자원과 병렬 코퍼스를 활용한 다국어 정보 검색 기법 개선에도 기여하고 있습니다. 연구는 실용적이고 비용 효율적인 정보 처리 솔루션 개발을 목표로 하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
No abstract available.
The goal of text categorization is to classify documents into a certain number of predefined categories. The previous works in this area have used a large number of labeled training documents for supervised learning. One problem is that it is difficult to create the labeled training documents. While it is easy to collect the unlabeled documents, it is not so easy to manually categorize them for creating training documents. In this paper, we propose an unsupervised learning method to overcome the
Automatic text categorization is a problem of automatically assigning text documents to predefined categories. In order to classify text documents, we must extract good features from them. In previous research, a text document is commonly represented by the term frequency and the inverted document frequency of each feature. Since there is a difference between important sentences and unimportant sentences in a document, the features from more important sentences should be considered more than oth
A wide range of supervised learning algorithms has been applied to Text Categorization. However, the supervised learning approaches have some problems. One of them is that they require a large, often prohibitive, number of labeled training documents for accurate learning. Generally, acquiring class labels for training data is costly, while gathering a large quantity of unlabeled data is cheap. We here propose a new automatic text categorization method for learning from only unlabeled data using
