Min-Yen Kan, Ph.D.
Affiliations: | Columbia University, New York, NY |
Area:
natural language processingGoogle:
"Min-Yen Kan"Mean distance: 21.1 (cluster 21) | S | N | B | C | P |
Parents
Sign in to add mentor| Kathleen R. Mckeown | grad student | 2003 | Columbia | |
| (Automatic text summarization as applied to information retrieval: Using indicative and informative summaries.) | ||||
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Publications
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| Nguyen VH, Sugiyama K, Kan MY, et al. (2020) Neural side effect discovery from user credibility and experience-assessed online health discussions. Journal of Biomedical Semantics. 11: 5 |
| An Y, Pan L, Kan M, et al. (2019) Resource Mention Extraction for MOOC Discussion Forums Ieee Access. 7: 87887-87900 |
| Prasad A, Kaur M, Kan M. (2018) Neural ParsCit: a deep learning-based reference string parser International Journal On Digital Libraries. 19: 323-337 |
| He X, Gao M, Kan M, et al. (2017) BiRank: Towards Ranking on Bipartite Graphs Ieee Transactions On Knowledge and Data Engineering. 29: 57-71 |
| Mayr P, Frommholz I, Cabanac G, et al. (2017) Introduction to the special issue on bibliometric-enhanced information retrieval and natural language processing for digital libraries (BIRNDL) International Journal On Digital Libraries. 19: 107-111 |
| Jaidka K, Chandrasekaran MK, Rustagi S, et al. (2017) Insights from CL-SciSumm 2016: the faceted scientific document summarization Shared Task International Journal On Digital Libraries. 19: 163-171 |
| Sugiyama K, Kan M. (2015) "Towards higher relevance and serendipity in scholarly paper recommendation" by Kazunari Sugiyama and Min-Yen Kan with Martin Vesely as coordinator Acm Sigweb Newsletter. 2015: 4 |
| Sugiyama K, Kan MY. (2014) A comprehensive evaluation of scholarly paper recommendation using potential citation papers International Journal On Digital Libraries. 16: 91-109 |
| Cho PS, Do HHN, Chandrasekaran MK, et al. (2013) Identifying research facilitators in an emerging Asian Research Area Scientometrics. 97: 75-97 |
| LIN Z, NG HT, KAN M. (2012) A PDTB-styled end-to-end discourse parser Natural Language Engineering. 20: 151-184 |