최은지 교수
Eun Ji Choi
고려대학교 국어국문학과 · 사회과학
연구실 소개
최은지 교수의 연구실은 다국어 환경에서의 한국어 교육과 디지털 언어 학습 자료 개발을 핵심으로 삼고 있습니다. 특히 외국인 유학생의 학술 구두 발표 능력 평가, 비즈니스 분야의 글쓰기 유형 분석, AI 기반 유연한 디지털 한국어 교재 설계 등 실용적이고 현장 중심의 연구를 진행하고 있습니다. 또한 다중모달 정보를 통합해 가짜 뉴스를 탐지하는 기술 개발을 통해 언어학과 인공지능의 융합 연구도 전개하고 있습니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
5Eun-ji ChoiMyungsook Jung. 2007. 10. 30. A Survey of Perceptions on Foreign Students' Academic Oral Presentation in Korean language. Bilingual Research 35, 303-332. This study conducted the survey regarding the perception on academic oral presentation in Korean language performed by foreign university or graduate students. 56 Koreans as the audience of academic oral presentation of foreign students and 28 foreign students who have performed the presentation more than twice participated in this s
This study investigates the types of writing tasks in main subject of Business administration. There are the most foreign students in the department of Business administration. And it is needed to conduct the research on the characteristics and aspects in that field. Especially it is worthy of notice that there would be differences in types of writing tasks between various study field. In these reasons, investigating was conducted to classify the types of writing tasks and estimate weight on tho
Detecting fake news has received a lot of attention. Many previous methods concatenate independently encoded unimodal data, ignoring the benefits of integrated multimodal information. Also, the absence of specialized feature extraction for text and images further limits these methods. This paper introduces an end-to-end model called TT-BLIP that applies the bootstrapping language-image pretraining for unified visionlanguage understanding and generation (BLIP) for three types for images, and bidi
Multimodal Fake News Detection has received increasing attention recently. Existing methods rely on independently encoded unimodal data and overlook the advantages of capturing intra-modality relationships and integrating inter-modal similarities using advanced techniques. To address these issues, Cross-Modal Tri-Transformer and Metric Learning for Multimodal Fake News Detection (CroMe) is proposed. CroMe utilizes Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Lan
This study explores development strategies for AI digital Korean materials aimed at ensuring flexibility. Language instructional materials development must account for the diverse characteristics of learners and the varied environments of educational fields. While developers have continuously strived to ensure this flexibility, paper-based materials have been constrained by a lack of adaptable methods and limited ease in module implementation. To address these limitations, this study proposes a
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