Research Groups

MACHINE LEARNING is the main research area of our laboratory. We are interested in applying DEEP LEARNING based techniques in a wide variety of fields. In particular, we focus on Natural Language Processing including ‘Sentimental Analysis’, ‘QA system’, ‘Chatbot’, and ‘Neural Machine Translation’. We are also doing research ‘Fake News Detection’ algorithm by utilizing Big Data on SNS. In addition, we perform a study on various application techniques like ‘Continual Learning’, ‘Object Detection Algorithm’ and ‘Crowdsourcing’.

■ Interests :
- Deep Learning
- Natural Language Processing, Neural Machine Translation
- Sentimental analysis, QA System, Chatbot
- Recommendation system
- Sequential Big Data trend forecasting and expandable learning algorithm
- Information diffusion analysis and fake news detection on SNS
- Image processing and object detection algorithm
- Crowdsourcing and statistical inference on graphical model
■ Project
- Conversation Situation and Emotion-aware Artificial Intelligence Dialog System Development (Ministry of Trade, Industry and Energy)
- PF-level Heterogeneous Ultra High Performance Computer Development (Ministry of Science, ICT and Future Planning)
- Development of Undiscovered Risk Information for Drugs Detectable Machine Learning Technique (National Research Foundation of Korea)
- A Study on English-Japanese Neural Machine Translation (NHN)
- Natural Language Processing System for Korean and English-Korean Machine Translation System Development (Institute of Engineering Research, Seoul National University)
- Smart Class Room : Machine Neural Translation (Samsung Advanced Institute of Technology)

[1] Seunghyun Yoon, Joongbo Shin, Kyomin Jung, Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering, Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2018.
[2] Woosang Lim, Bo Dai, Rundong Du, Kyomin Jung, Le Song, Haesun Park, Multi-scale Nystrom Method, International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
[3] Joongbo Shin, Yanghoon Kim, Seunghyun Yoon, Kyomin Jung, Contextual-CNN: A
Novel Architecture Capturing Unified Meaning for Sentence Classification, IEEE International Conference on Big Data and Smart Computing (BigComp), 2018.
[4] Heeyoung Kwak, Joonyoung Kim, Yongsub Lim, Shin-Kap Han, and Kyomin Jung,
Centrality Fairness: Measuring and Analyzing Structural Inequality of Online Social Network, Journal of Internet Technology (JIT), 2017.
[5] Seunghyun Yoon, Pablo Estrada, Kyomin Jung, Synonym Discovery with Etymology-based Word Embeddings, IEEE Symposium Series on Computational Intelligence (SSCI), 2017.
[6] Woosang Lim, Jungsoo Lee, Yongsub Lim, Doo-Hwan Bae, Haesun Park, Dae-Shik Kim, and Kyomin Jung, Hierarchical Ordering with Partial Pairwise Hierarchical Relationships on the Macaque Brain Data Sets, PLOS ONE, 2017.