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Qing Yang
Publications - 13
Citations - 28
Qing Yang is an academic researcher. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 1, co-authored 1 publications receiving 1 citations.
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Proceedings ArticleDOI
Combining Explicit Entity Graph with Implicit Text Information for News Recommendation
TL;DR: Wang et al. as discussed by the authors proposed a news recommendation approach, which combine explicit entity graph with implicit text information, which consists of two types of nodes and three kinds of edges, which represent chronological order, related and affiliation relationship.
Proceedings ArticleDOI
TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation
TL;DR: This paper proposes a novel transition-based method, TranS, for knowledge graph embedding, where the single relation vector in traditional scoring patterns is replaced with synthetic relation representation, which can solve issues effectively and efficiently.
Proceedings ArticleDOI
DeepVT: Deep View-Temporal Interaction Network for News Recommendation
TL;DR: This paper focuses on the view-level information for user modeling and proposes Deep View-Temporal Interaction Network (DeepVT) for news recommendation, which mainly contains two components, i.e., 2D semi-causal convolutional neural network (SC-CNN) and multi-operator attention (MoA).
Journal ArticleDOI
Extracting Spatio-Temporal Information from Chinese Archaeological Site Text
TL;DR: The study demonstrates that the information extraction method proposed in this paper is feasible for the Chinese archaeological site texts, which promotes the establishment of knowledge graphs in archaeology and provides new methods and ideas for the development of information mining technology in archaeological technology.
Proceedings ArticleDOI
Efficient Non-sampling Expert Finding
TL;DR: This paper proposes a novel Efficient Non-sampling Expert Finding model, named ENEF, which could learn accurate representations of questions and experts from whole training data and could achieve better performance and faster training efficiency than existing state-of-the-art expert finding methods.