62 citations · 211 across the 14 of their papers we have counts for
7 papers · 1 filter
Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs
Chao Shang, Guangtao Wang, Peng Qi +1
Question answering over temporal knowledge graphs (KGs) efficiently uses facts contained in a temporal KG, which records entity relations and when they occur in time, to answer nat…
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification
Xiaochen Hou, Peng Qi, Guangtao Wang +4
Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks(GNN), b…
Entity and Evidence Guided Relation Extraction for DocRED
Kevin Huang, Guangtao Wang, Tengyu Ma +1
Document-level relation extraction is a challenging task which requires reasoning over multiple sentences in order to predict relations in a document. In this paper, we pro-pose a…
Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding
Yun Tang, Jing Huang, Guangtao Wang +2
Translational distance-based knowledge graph embedding has shown progressive improvements on the link prediction task, from TransE to the latest state-of-the-art RotatE. However, N…
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
Ming Tu, Kevin Huang, Guangtao Wang +3
Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining th…
Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification
Xiaochen Hou, Jing Huang, Guangtao Wang +2
Aspect-level sentiment classification aims to identify the sentiment polarity towards a specific aspect term in a sentence. Most current approaches mainly consider the semantic inf…
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
Ming Tu, Guangtao Wang, Jing Huang +3
Multi-hop reading comprehension (RC) across documents poses new challenge over single-document RC because it requires reasoning over multiple documents to reach the final answer. I…