G
Gang Tian
Researcher at Wuhan University
Publications - 12
Citations - 255
Gang Tian is an academic researcher from Wuhan University. The author has contributed to research in topics: Topic model & Relationship extraction. The author has an hindex of 6, co-authored 12 publications receiving 193 citations.
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Journal ArticleDOI
Personalized app recommendation based on app permissions
TL;DR: An app risk score calculating method ARSM based on app-permission bipartite graph model, and a novel matrix factorization algorithm MFPF based on users’ interests, apps’ permissions and functionalities to handle personalized app recommendation are proposed.
Journal ArticleDOI
Incorporating word embeddings into topic modeling of short text
TL;DR: A novel model for short text topic modeling, referred as Conditional Random Field regularized Topic Model (CRFTM), which not only develops a generalized solution to alleviate the sparsity problem by aggregating short texts into pseudo-documents, but also leverages a Conditional random field regularized model that encourages semantically related words to share the same topic assignment.
Journal ArticleDOI
Mining Event-Oriented Topics in Microblog Stream with Unsupervised Multi-View Hierarchical Embedding
TL;DR: Experimental results on TREC Tweets2011 dataset and Sina Weibo dataset demonstrate that the UMHE framework can construct hierarchical structure with high fitness, but also yield topic embeddings with salient semantics; therefore, it can derive event-oriented topics with meaningful descriptions.
Proceedings ArticleDOI
Neural Sparse Topical Coding
TL;DR: A novel sparsity-enhanced topic model, Neural Sparse Topical Coding (NSTC) is proposed, which focuses on replacing the complex inference process with the back propagation, which makes the model easy to explore extensions and illustrate the flexibility offered by the neural network based framework.
Journal ArticleDOI
Bayesian Sparse Topical Coding
TL;DR: This paper proposes a novel Bayesian hierarchical topic models called Bayesian Sparse Topical Coding with Poisson Distribution (BSTC-P), and adopts superior hierarchical sparse inducing prior, with the purpose of achieving the sparsest optimal solution.