I majored in natural language processing, which enables artificial intelligence to understand and reponse to human speech. Specifically, I have studied and developed language understanding, data augmentation methods, question-answering systems and large-scale language models. My future research aims to address ethical issues inherent in text data and language models to create the ultimate artificial intelligence.

저는 인공지능이 사람의 말을 이해하고 응답 할 수 있게 하는 자연어처리를 전공했습니다. 특히, 언어이해 기술, 데이터 확장 방법, 질의 응답 시스템과 거대 언어모델을 활용하는 방법을 연구/개발 해왔습니다. 앞으로는 궁극적인 인공지능을 만들기 위해 텍스트와 언어모델에 포함된 윤리적 문제를 극복하는 연구도 진행할 예정입니다.

최종학력

Ph.D. in Computer Science, Sogang University

전공분야

Natural Language Processing

주요 연구

Machine Learning for Natural Language Processing, Semi-supervised Learning, Question Answering System

주요 강의

• 실용자연어처리
• 영어 데이터 처리를 위한 프로그래밍

주요 논문/저서

(International Journal)

• Juae Kim, Yejin Kim, Sangwoo Kang, Jungyun Seo. (2022). Weakly Labeled Data Augmentation for Social Media Named Entity Recognition. Expert systems with applications. (SCI – Q1) (IF: 8.665) 
  
  
• Juae Kim, Yejin Kim, Sangwoo Kang. (2021). Adaptive Named Entity Recognition Using Distant Supervision for Contemporary Written Texts. IEEE Access, 9, 80405-80414 (SCIE) (IF: 3.367)
  
  
• Juae Kim, Youngjoong Ko, Jungyun Seo. (2020). Construction of Machine-Labeled Data for Improving Named Entity Recognition by Transfer Learning. IEEE Access, 8, 59684-59693. (SCIE) (IF: 3.367)
  
  
• Juae Kim, Youngjoong Ko, Jungyun Seo. (2019). A Bootstrapping Approach with CRF and Deep Learning Models for Improving the Biomedical Named Entity Recognition in Multi-domains. IEEE Access, 7, 70308-70318, (SCIE) (IF: 3.367)
  
  

(International Conference)

• Cheoneum Park, Juae Kim. (2023) Robust Multi-task Learning-based Korean POS Tagging to Overcome Word Spacing Errors. ACM Transactions on Asian and Low-Resource Language Information Processing. (SCI(E), IF: 2.0)

• Cheoneum Park, Seohyeong Jeong, Juae Kim. (2023). ADMit: Improving NER in automotive domain with domain adversarial training and multi-task learning. Expert systems with applications. (SCI(E), IF: 8.665)

• Minwoo Lee, Seungpil Won, Juae Kim, Hwanhee Lee, Cheoneum Park, Kyomin Jung. (2021). CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Models. Proceedings of CIKM 2021.

• *Bosung Kim, Juae Kim (co-first author), Youngjoong Ko, Jungyun Seo (2021). Commonsense Knowledge Augmentation for Low-Resource Languages via Adversarial Learning. Proceedings of AAAI-2021. 

• *Yejin Kim, Juae Kim (co-first author), Jungyun Seo. (2020). Noise Improves Noise: Verification of Pre-training Effect with Weakly Labeled Data on Social Media NER. Proceedings of the Big Data and Smart Computing (BigComp), Busan, Korea.

• *Cheoneum Park, Juae Kim (co-first author), Hyeon-gu Lee, Reinald Kim Amplayo, Harksoo Kim, Jungyun Seo, Changki Lee. (2019). ThisIsCompetition at SemEval-2019 Task 9:BERT is unstable for out-of-domain samples. Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), Minneapolis, USA. 

• Juae Kim, Youngjoong Ko , Jungyun Seo. (2019). Transfer Learning from Automatically Annotated Data for Recognizing Named Entities in Recent Generated Texts. Proceedings of the Big Data and Smart Computing (BigComp) 2019, Kyoto, Japan. 

• Hwijeen Ahn, Minyoung Seo, Chanmin Park, Juae Kim , Jungyun Seo. (2019). Extensive Use of Morpheme Featrues in Korean Dependency Parsing. Proceedings of the Big Data and Smart Computing (BigComp) 2019, Kyoto, Japan. 

• Juae Kim, Soonjae Kwon, Youngjoong Ko , Jungyun Seo. (2017). A Method to Generate a Machine-labeled Data for Biomedical Named Entity Recognition with Various Sub-Domains. Proceedings of International Workshop on Digital Disease Detection Using Social Media (IJCNLP 2017), Taiwan. 

• Hyeon-gu Lee, Minkyung Kim, Harksoo Kim, Juae Kim, Sunjae Kwon, Jungyun Seo, Jungkyu Choi, Yi-reun Kim. (2016). KSAnswer: Question-answering System of Kangwon National University and Sogang University in the 2016 BioASQ Challenge. Proceedings of the BioASQ Challenge Workshop, Berlin, Germany.