KAN Min Yen

Associate Professor
Assistant Dean, Undergraduate Studies

Ph.D. (Computer Science, Columbia University, 2002)
M.Sc. (Computer Science, Columbia University, 1998)
B.S. (Computer Science, Columbia University, 1996)
651 61885

Research Areas

  • Media

Research Interests

  • Natural Language Processing
  • Digital Libraries
  • Applied Machine Learning
  • Information Retrieval
  • Human-Computer Interaction


Min-Yen Kan (BS;MS;PhD Columbia Univ.) is an associate professor at the National University of Singapore. He is a senior member of the ACM and a member of the IEEE. Currently, he is an associate editor for the journal "Information Retrieval" and is the Editor for the ACL Anthology, the computational linguistics community's largest archive of published research. His research interests include digital libraries and applied natural language processing. Specific projects include work in the areas of scientific discourse analysis, full-text literature mining, machine translation and applied text summarization.

Current Projects

  • Mobile App Recommendation: This project aims to build recommendation systems for App Stores. The model is developed from a graph-based approach, and it utilises Twitter information which can precede formal user ratings in app stores, as well as version information which is specific to mobile apps.
  • MOOC Wikification: This project aims to build a system which is able to identify the resources mentioned and referenced in the discussion forums of MOOC platforms and link to the actual location automatically. It provides learners the ability to combine all resources in a more convenient way.
  • NER In Legal Domain: This project is in collaboration with INTELLLEX (a tech start-up for law), and aims to increase the precision of existing Named Entity Recognition systems. While not restricting on the types of people, location, etc, the project has been extended to the scope of Legal terms.
  • A Web Based Dashboard for MOOC Instructors: This project is proposed in assistance of the Instructor Intervene in MOOC Discussion Forums [link], and aims to build a system which takes in generic forum threads (from Coursera, edX, etc), and outputs the threads in the order of importance such that instructors are able to intervene on time.
  • Verb Duration Discovery: This project aims to discover the relationships between verbs and durations. So if given a verb and a situation, we can predict how long the action will last. For example, given a verb “eat”, a situation “I eat sandwich”, we can predict the action “eat” will last for a couple of minutes.
  • Snippet Generation for MOOC Discussion Forums: This project aims to identify relevant sentences that are significant in MOOC Discussion Forum threads to generate a summary.
  • Investigating Instructor Intervention in MOOC Discussion Forums: This project aims to design predictive models to identify important threads from MOOC discussion forums. It may allow building of dashboards to automatically prompt instructors of MOOC on when and how to intervene in discussion forums such that good pedagogical practices can be scaled in the context of MOOC.
  • Implicit Discourse Relation Recognition: This project aims to leverage on both traditional feature-based and deep learning approaches to improve the recognition performance of PDTB style implicit discourse relation such that it can be made viable for real world applications.

Selected Publications

  • Kishaloy Halder, Lahari Poddar and Min-Yen Kan (2017) Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach. In Proceedings of 8th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA '17). September 2017. Copenhagen, Denmark.

  • Wenqiang Lei, Xuancong Wang, Meichun Liu, Ilija Ilievski, Xiangnan He and Min-Yen Kan (2017) SWIM: A Simple Word Interaction Model for Implicit Discourse Relation Recognition. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI '17), August 2017, Melbourne, Australia.

  • Muthu Kumar Chandrasekaran, Carrie Demmans Epp, Min-Yen Kan and Diane Litman (2017). Using Discourse Signals for Robust Instructor Intervention Prediction. In Proceedings of the Thirty-First AAAI conference on Artificial Intelligence (AAAI-17), San Francisco, USA, 3415-3421, AAAI.

  • Tao Chen, Xiangnan He and Min-Yen Kan (2016). Context-aware Image Tweet Modelling and Recommendation. In Proceedings of the 24th ACM International Conference on Multimedia (MM'16), Amsterdam, The Netherlands. 15-19 Oct.

  • Xiangnan He, Hanwang Zhang, Min-Yen Kan and Tat-Seng Chua (2016).
    Fast Matrix Factorization for Online Recommendation with Implicit Feedback. In Proceedings of Special Interest Group on Information Retrieval (SIGIR '16). Pisa, Italy. July 17-21.

  • Bang Hui Lim, Dongyuan Lu, Tao Chen and Min-Yen Kan (2015).
    #mytweet via Instagram: Exploring User Behaviour across Multiple Social Networks. In Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM '15), Paris, France.

  • Tao Chen, Naijia Zheng, Yue Zhao, Muthu Kumar Chandrasekaran, Min-Yen Kan (2015). Interactive Second Language Learning from News Websites. In Proceedings of 2nd Workshop on Natural Language Processing Techniques for Educational Applications (NLP-TEA '15), Beijing, China.

  • Muthu Kumar Chandrasekaran, Min-Yen Kan, Kiruthika Ragupathi and Bernard C. Y. Tan (2015). Learning instructor intervention from MOOC
    forums: Early Results and Issues. In Proceedings of Education Data Mining (EDM '15), Madrid, Spain.

Awards & Honours

  • Vannevar Bush Best Paper Award, 2012, Joint Conference on Digital Libraries (JCDL 2012)
  • 1st place in automated ROUGE measures among all teams, TAC 2011 Guided Summarization task, Text Analysis Conference
  • Senior Member ACM

Teaching (2018/2019)

  • CS3244: Machine Learning