2009 - ACM Senior Member
His primary areas of investigation include Information retrieval, Artificial intelligence, Natural language processing, Ranking and Data mining. His Information retrieval study combines topics from a wide range of disciplines, such as Web resource and World Wide Web. When carried out as part of a general Artificial intelligence research project, his work on Paraphrase is frequently linked to work in Weighting, therefore connecting diverse disciplines of study.
The study incorporates disciplines such as Discourse relation and Segmentation in addition to Natural language processing. His Ranking research focuses on Ranking and how it connects with Relation, Mutual information and Question answering. His Data mining study integrates concerns from other disciplines, such as Feature, Cluster analysis, Fuzzy clustering, Query expansion and Component.
His primary scientific interests are in Information retrieval, Artificial intelligence, Natural language processing, World Wide Web and Digital library. His work deals with themes such as Metadata and Web page, which intersect with Information retrieval. His Artificial intelligence study combines topics in areas such as Context and Machine learning.
His research in Natural language processing intersects with topics in Speech recognition, Word and Vocabulary. His study ties his expertise on User interface together with the subject of World Wide Web. His Multi-document summarization study in the realm of Automatic summarization interacts with subjects such as Information technology.
Min-Yen Kan mostly deals with Artificial intelligence, Information retrieval, Natural language processing, Question generation and World Wide Web. His Artificial intelligence research focuses on Variation and how it relates to Embedding. When carried out as part of a general Information retrieval research project, his work on Recommender system is frequently linked to work in Modal, therefore connecting diverse disciplines of study.
The various areas that Min-Yen Kan examines in his Natural language processing study include Dependency, Context, Comprehension and Inflection. In his work, Relevance and Data science is strongly intertwined with Forcing, which is a subfield of Question generation. Min-Yen Kan interconnects Question answering, Thread and Anchoring in the investigation of issues within World Wide Web.
Information retrieval, Natural language processing, Artificial intelligence, Question generation and African American Vernacular English are his primary areas of study. His Information retrieval research is multidisciplinary, relying on both Schema and SQL. Particularly relevant to Parsing is his body of work in Natural language processing.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Style and Dialog box. His Question generation research is multidisciplinary, incorporating perspectives in Variety, Cognitive science and Human intelligence. His studies in Recommender system integrate themes in fields like Reflection and Conversation.
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Fast Matrix Factorization for Online Recommendation with Implicit Feedback
Xiangnan He;Hanwang Zhang;Min-Yen Kan;Tat-Seng Chua.
international acm sigir conference on research and development in information retrieval (2016)
SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles
Su Nam Kim;Olena Medelyan;Min-Yen Kan;Timothy Baldwin.
meeting of the association for computational linguistics (2010)
ParsCit: an Open-source CRF Reference String Parsing Package
Isaac G. Councill;C. Lee Giles;Min Yen Kan.
language resources and evaluation (2008)
TriRank: Review-aware Explainable Recommendation by Modeling Aspects
Xiangnan He;Tao Chen;Min-Yen Kan;Xiao Chen.
conference on information and knowledge management (2015)
Keyphrase extraction in scientific publications
Thuy Dung Nguyen;Min-Yen Kan.
international conference on asian digital libraries (2007)
Fast webpage classification using URL features
Min-Yen Kan;Hoang Oanh Nguyen Thi.
conference on information and knowledge management (2005)
Recognizing Implicit Discourse Relations in the Penn Discourse Treebank
Ziheng Lin;Min-Yen Kan;Hwee Tou Ng.
empirical methods in natural language processing (2009)
Question answering passage retrieval using dependency relations
Hang Cui;Renxu Sun;Keya Li;Min-Yen Kan.
international acm sigir conference on research and development in information retrieval (2005)
A PDTB-Styled End-to-End Discourse Parser
Ziheng Lin;Hwee Tou Ng;Min-Yen Kan.
Natural Language Engineering (2014)
The ACL Anthology Reference Corpus: A Reference Dataset for Bibliographic Research in Computational Linguistics
Steven Bird;Robert Dale;Bonnie J. Dorr;Bryan R. Gibson.
language resources and evaluation (2008)
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