Zobrazeno 61 - 70
of 361
pro vyhledávání: '"Zhang, Ningyu"'
Autor:
Deng, Shumin, Wang, Chengming, Li, Zhoubo, Zhang, Ningyu, Dai, Zelin, Chen, Hehong, Xiong, Feiyu, Yan, Ming, Chen, Qiang, Chen, Mosha, Chen, Jiaoyan, Pan, Jeff Z., Hooi, Bryan, Chen, Huajun
Business Knowledge Graphs (KGs) are important to many enterprises today, providing factual knowledge and structured data that steer many products and make them more intelligent. Despite their promising benefits, building business KG necessitates solv
Externí odkaz:
http://arxiv.org/abs/2209.15214
Skeleton-based human action recognition is a longstanding challenge due to its complex dynamics. Some fine-grain details of the dynamics play a vital role in classification. The existing work largely focuses on designing incremental neural networks w
Externí odkaz:
http://arxiv.org/abs/2209.02986
Autor:
Chen, Zhuo, Huang, Yufeng, Chen, Jiaoyan, Geng, Yuxia, Fang, Yin, Pan, Jeff, Zhang, Ningyu, Zhang, Wen
Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge. Most existing methods exploit pre-trained language models or/and unstructured text, but the knowledge in th
Externí odkaz:
http://arxiv.org/abs/2207.12888
Autor:
Chen, Xiang, Li, Lei, Zhang, Ningyu, Liang, Xiaozhuan, Deng, Shumin, Tan, Chuanqi, Huang, Fei, Si, Luo, Chen, Huajun
Prompt learning approaches have made waves in natural language processing by inducing better few-shot performance while they still follow a parametric-based learning paradigm; the oblivion and rote memorization problems in learning may encounter unst
Externí odkaz:
http://arxiv.org/abs/2205.14704
Existing data-centric methods for protein science generally cannot sufficiently capture and leverage biology knowledge, which may be crucial for many protein tasks. To facilitate research in this field, we create ProteinKG65, a knowledge graph for pr
Externí odkaz:
http://arxiv.org/abs/2207.10080
Transformers have achieved remarkable performance in widespread fields, including natural language processing, computer vision and graph mining. However, vanilla Transformer architectures have not yielded promising improvements in the Knowledge Graph
Externí odkaz:
http://arxiv.org/abs/2205.10852
Autor:
Qu, Yincen, Zhang, Ningyu, Chen, Hui, Dai, Zelin, Xu, Zezhong, Wang, Chengming, Wang, Xiaoyu, Chen, Qiang, Chen, Huajun
In e-commerce, the salience of commonsense knowledge (CSK) is beneficial for widespread applications such as product search and recommendation. For example, when users search for ``running'' in e-commerce, they would like to find products highly rela
Externí odkaz:
http://arxiv.org/abs/2205.10843
Autor:
Chen, Xiang, Zhang, Ningyu, Li, Lei, Yao, Yunzhi, Deng, Shumin, Tan, Chuanqi, Huang, Fei, Si, Luo, Chen, Huajun
Multimodal named entity recognition and relation extraction (MNER and MRE) is a fundamental and crucial branch in information extraction. However, existing approaches for MNER and MRE usually suffer from error sensitivity when irrelevant object image
Externí odkaz:
http://arxiv.org/abs/2205.03521
Autor:
Chen, Xiang, Zhang, Ningyu, Li, Lei, Deng, Shumin, Tan, Chuanqi, Xu, Changliang, Huang, Fei, Si, Luo, Chen, Huajun
Multimodal Knowledge Graphs (MKGs), which organize visual-text factual knowledge, have recently been successfully applied to tasks such as information retrieval, question answering, and recommendation system. Since most MKGs are far from complete, ex
Externí odkaz:
http://arxiv.org/abs/2205.02357
Pre-trained language models have contributed significantly to relation extraction by demonstrating remarkable few-shot learning abilities. However, prompt tuning methods for relation extraction may still fail to generalize to those rare or hard patte
Externí odkaz:
http://arxiv.org/abs/2205.02355