J
Jiawei Han
Researcher at University of Illinois at Urbana–Champaign
Publications - 1302
Citations - 155054
Jiawei Han is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Cluster analysis & Knowledge extraction. The author has an hindex of 168, co-authored 1233 publications receiving 143427 citations. Previous affiliations of Jiawei Han include Georgia Institute of Technology & United States Army Research Laboratory.
Papers
More filters
Proceedings ArticleDOI
EVIDENCEMINER: Textual Evidence Discovery for Life Sciences.
Xuan Wang,Yingjun Guan,Weili Liu,Aabhas Chauhan,Enyi Jiang,Qi Li,David A. Liem,Dibakar Sigdel,John Caufield,Peipei Ping,Jiawei Han +10 more
TL;DR: EVIDENCEMINER is a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences, supported by novel data-driven methods for distantly supervised named entity recognition and open information extraction.
Posted Content
Comparative Document Analysis for Large Text Corpora
TL;DR: This paper uses a general graph-based framework to derive novel measures on phrase semantic commonality and pairwise distinction, where the background corpus is used for computing phrase-document semantic relevance.
Proceedings ArticleDOI
Arabic Named Entity Recognition: What Works and What's Next.
TL;DR: This paper presents the winning solution to the Arabic Named Entity Recognition challenge run by Topcoder.com and observes that representation learning modules can significantly boost the performance but requires a proper pre-processing and the resulting embedding can be further enhanced with feature engineering due to the limited size of the training data.
Knowledge discovery in object-oriented databases: the first step
TL;DR: This paper proposes the first step towards knowledge discovery in object-oriented databases by extension of the attribute-oriented induction technique from relational databases to object- oriented databases and shows that knowledge discovery will substantially enhance the power and flexibility of querying data and knowledge in object -oriented databases.
Journal ArticleDOI
A probabilistic approach to detect mixed periodic patterns from moving object data
TL;DR: A probabilistic periodicity detection method called MPDA is proposed that first identifies high dense regions by the kernel density method, then generates revisit time sequences based on the dense regions, and at last adopts a filter-refine paradigm to detect mixed periodicities.