no code implementations • NAACL (TextGraphs) 2021 • Yuejia Xiang, Yunyan Zhang, Xiaoming Shi, Bo Liu, Wandi Xu, Xi Chen
Then, a selection module is employed to choose those most relative facts in an autoregressive manner, giving a preliminary order for the retrieved facts.
no code implementations • COLING 2022 • Bo Liu, Wandi Xu, Yuejia Xiang, XiaoJun Wu, Lejian He, BoWen Zhang, Li Zhu
However, we find that noise learning in text classification is relatively underdeveloped: 1. many methods that have been proven effective in the image domain are not explored in text classification, 2. it is difficult to conduct a fair comparison between previous studies as they do experiments in different noise settings.
no code implementations • 17 Feb 2023 • Yangning Li, Jiaoyan Chen, Yinghui Li, Yuejia Xiang, Xi Chen, Hai-Tao Zheng
Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering.
no code implementations • 8 Sep 2021 • Baoyu Jing, Shengyu Feng, Yuejia Xiang, Xi Chen, Yu Chen, Hanghang Tong
X-GOAL is comprised of two components: the GOAL framework, which learns node embeddings for each homogeneous graph layer, and an alignment regularization, which jointly models different layers by aligning layer-specific node embeddings.
no code implementations • SEMEVAL 2021 • Jiarun Cao, Yuejia Xiang, Yunyan Zhang, Zhiyuan Qi, Xi Chen, Yefeng Zheng
Accordingly, we propose CONNER, a cascade count and measurement extraction tool that can identify entities and the corresponding relations in a two-step pipeline model.
no code implementations • NAACL 2021 • Junjie Luo, Xi Chen, Jichao Sun, Yuejia Xiang, Ningyu Zhang, Xiang Wan
Word representations empowered with additional linguistic information have been widely studied and proved to outperform traditional embeddings.
1 code implementation • Findings (ACL) 2021 • Yuejia Xiang, Ziheng Zhang, Jiaoyan Chen, Xi Chen, Zhenxi Lin, Yefeng Zheng
Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities.
1 code implementation • 12 May 2021 • Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen, Xi Chen, Yuejia Xiang, Ningyu Zhang, Yefeng Zheng
Knowledge Graph (KG) alignment is to discover the mappings (i. e., equivalent entities, relations, and others) between two KGs.
1 code implementation • COLING 2020 • Ziheng Zhang, Jiaoyan Chen, Xi Chen, Hualuo Liu, Yuejia Xiang, Bo Liu, Yefeng Zheng
Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of unbiased seed mappings for training and validation, which significantly limits their usage.