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
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Proceedings ArticleDOI
On Integrating Network and Community Discovery
TL;DR: This work will discuss algorithms for integrating community detection with network discovery tightly integrate with the cost of actually discovering a network with the community detection process, so that the two processes can support each other and are performed in a mutually cohesive way.
Posted Content
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Xuan Wang,Yu Zhang,Xiang Ren,Yuhao Zhang,Marinka Zitnik,Jingbo Shang,Curtis P. Langlotz,Jiawei Han +7 more
TL;DR: This article proposed a multi-task learning framework for BioNER to collectively use the training data of different types of entities and improve the performance on each of them in experiments on 15 benchmark BioNER datasets, achieving substantially better performance compared with state-of-the-art BioNER systems and baseline neural sequence labeling models.
Proceedings ArticleDOI
SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery
TL;DR: This work hypothesizes that these two tasks are tightly coupled because two synonymous entities tend to have similar likelihoods of belonging to various semantic classes, and designs SynSetExpan, a novel framework that enables two tasks to mutually enhance each other.
Proceedings ArticleDOI
Is Objective Function the Silver Bullet? A Case Study of Community Detection Algorithms on Social Networks
TL;DR: Methods of measurements are divided in to two categories, according to whether they rely on ground-truth or not, to answer whether these general used objective functions are well consistent with the real performance of community detection algorithms across a number of homogeneous and heterogeneous networks.
Journal ArticleDOI
Mining strong relevance between heterogeneous entities from unstructured biomedical data
TL;DR: This paper builds an entity correlation graph from data, in which the collection of paths linking two heterogeneous entities offer rich semantic contexts for their relationships, especially those paths following the patterns of top-$$k$$k selected meta paths inferred from data.