1 code implementation • COLING 2022 • Minghao Tang, Peng Zhang, Yongquan He, Yongxiu Xu, Chengpeng Chao, Hongbo Xu
Cross-domain named entity recognition aims to improve performance in a target domain with shared knowledge from a well-studied source domain.
Cross-Domain Named Entity Recognition Machine Reading Comprehension +2
no code implementations • 15 Mar 2024 • Yongquan He, Xuancheng Huang, Minghao Tang, Lingxun Meng, Xiang Li, Wei Lin, Wenyuan Zhang, Yifu Gao
Recent methods try to alleviate the CF problem by modifying models or replaying data, which may only remember the surface-level pattern of instructions and get confused on held-out tasks.
no code implementations • 26 Feb 2024 • Yifu Gao, Linbo Qiao, Zhigang Kan, Zhihua Wen, Yongquan He, Dongsheng Li
Temporal knowledge graph question answering (TKGQA) poses a significant challenge task, due to the temporal constraints hidden in questions and the answers sought from dynamic structured knowledge.
no code implementations • 21 Feb 2024 • Yongquan He, Zihan Wang, Peng Zhang, Zhaopeng Tu, Zhaochun Ren
To address this issue, recent works apply the graph neural network on the existing neighbors of the unseen entities.
1 code implementation • 19 Feb 2024 • Yongquan He, Peng Zhang, Luchen Liu, Qi Liang, Wenyuan Zhang, Chuang Zhang
In recent years, temporal knowledge graph (TKG) reasoning has received significant attention.
1 code implementation • 23 Oct 2023 • Minghao Tang, Yongquan He, Yongxiu Xu, Hongbo Xu, Wenyuan Zhang, Yang Lin
Fine-grained entity typing (FET) is an essential task in natural language processing that aims to assign semantic types to entities in text.
1 code implementation • 23 Oct 2023 • Minghao Tang, Yongquan He, Yongxiu Xu, Hongbo Xu, Wenyuan Zhang, Yang Lin
By leveraging the guiding semantics of boundary offsets, BOPN establishes connections between non-entity and entity spans, enabling non-entity spans to function as additional positive samples for entity detection.
1 code implementation • 17 May 2023 • Zihan Wang, Kai Zhao, Yongquan He, Zhumin Chen, Pengjie Ren, Maarten de Rijke, Zhaochun Ren
Recent work on knowledge graph completion (KGC) focused on learning embeddings of entities and relations in knowledge graphs.
1 code implementation • 28 Jul 2022 • Xiaohan Xu, Peng Zhang, Yongquan He, Chengpeng Chao, Chaoyang Yan
Inductive link prediction for knowledge graph aims at predicting missing links between unseen entities, those not shown in training stage.