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Xiaojun Quan
Researcher at Sun Yat-sen University
Publications - 64
Citations - 1873
Xiaojun Quan is an academic researcher from Sun Yat-sen University. The author has contributed to research in topics: Question answering & Context (language use). The author has an hindex of 18, co-authored 64 publications receiving 1139 citations. Previous affiliations of Xiaojun Quan include Institute for Infocomm Research Singapore & University of Science and Technology of China.
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Relational Graph Attention Network for Aspect-based Sentiment Analysis
TL;DR: This paper defines a unified aspect-oriented dependency tree structure rooted at a target aspect by reshaping and pruning an ordinary dependency parse tree and proposes a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction.
Proceedings ArticleDOI
Relational Graph Attention Network for Aspect-based Sentiment Analysis
TL;DR: This article proposed a relational graph attention network (R-GATN) to encode the new tree structure for sentiment prediction. But, due to the complexity of language and the existence of multiple aspects in a single sentence, these models often confuse the connections.
Proceedings Article
Short and sparse text topic modeling via self-aggregation
TL;DR: A novel model integrating topic modeling with short text aggregation during topic inference is presented, founded on general topical affinity of texts rather than particular heuristics, making the model readily applicable to various short texts.
Journal ArticleDOI
Feature selection for high-dimensional imbalanced data
TL;DR: The experimental results show that both decomposition-based and Hellinger distance-based methods can outperform existing feature-selection methods with a clear margin on imbalanced data.
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UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2
TL;DR: Thorough analyses demonstrate that the session-level training sequence formulation and the generated dialog context are essential for UBAR to operate as a fully end-to-end task-oriented dialog system in real life.