1 code implementation • 3 Feb 2024 • Wentao Ding, Jinmao Li, Liangchuan Luo, Yuzhong Qu
We propose Evidence Pattern Retrieval (EPR) to explicitly model the structural dependencies during subgraph extraction.
no code implementations • 18 Dec 2023 • Jianhao Chen, Junyang Ren, Wentao Ding, Haoyuan Ouyang, Wei Hu, Yuzhong Qu
Temporal facts, which are used to describe events that occur during specific time periods, have become a topic of increased interest in the field of knowledge graph (KG) research.
1 code implementation • 18 Apr 2023 • Jianhao Chen, Junyang Ren, Wentao Ding, Yuzhong Qu
Temporal facts, the facts for characterizing events that hold in specific time periods, are attracting rising attention in the knowledge graph (KG) research communities.
no code implementations • 10 Oct 2022 • Wentao Ding, Hao Chen, Huayu Li, Yuzhong Qu
Answering factual questions with temporal intent over knowledge graphs (temporal KGQA) attracts rising attention in recent years.
1 code implementation • Findings (EMNLP) 2021 • Wentao Ding, Jianhao Chen, Jinmao Li, Yuzhong Qu
The understanding of time expressions includes two sub-tasks: recognition and normalization.
no code implementations • 22 Mar 2021 • Yi-Shuai Niu, Wentao Ding, Junpeng Hu, Wenxu Xu, Stephane Canu
We established a Spatio-Temporal Neural Network, namely STNN, to forecast the spread of the coronavirus COVID-19 outbreak worldwide in 2020.
no code implementations • 2 Feb 2020 • Yi-Shuai Niu, Yu You, Wenxu Xu, Wentao Ding, Junpeng Hu, Songquan Yao
In this paper, we design a hybrid extractive sentence compression model combining a probability language model and a parse tree language model for compressing sentences by guaranteeing the syntax correctness of the compression results.