Results 281 to 290 of about 2,936,543 (297)

Multiple Embeddings Enhanced Multi-Graph Neural Networks for Chinese Healthcare Named Entity Recognition

IEEE journal of biomedical and health informatics, 2021
Named Entity Recognition (NER) is a natural language processing task for recognizing named entities in a given sentence. Chinese NER is difficult due to the lack of delimited spaces and conventional features for determining named entity boundaries and ...
Lung-Hao Lee, Yi Lu
semanticscholar   +1 more source

Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents

International Conference on Pattern Recognition, 2021
The use of administrative documents to communicate and leave record of business information requires of methods able to automatically extract and understand the content from such documents in a robust and efficient way.
Manuel Carbonell, Pau Riba, M. Villegas, A. Fornés, J. Lladós   +4 more
semanticscholar   +1 more source

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020
Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood.
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang   +5 more
semanticscholar   +1 more source

Word-Character Graph Convolution Network for Chinese Named Entity Recognition

IEEE/ACM Transactions on Audio Speech and Language Processing, 2020
Recent researches try to integrate word information into the character-based Chinese NER by modifying the structure of the standard BiLSTM-CRF model. They follow the paradigm of explicitly modeling forward and backward sequences, adopting an LSTM variant
Zhuo Tang, Boyan Wan, Li Yang
semanticscholar   +1 more source

Dynamic Graph Construction Framework for Multimodal Named Entity Recognition in Social Media

IEEE Transactions on Computational Social Systems
Multimodal named entity recognition (MNER) aims to detect named entities and identify the entity types based on texts and attached images, which also generates inputs for other comprehensive tasks, such as multimodal machine translation, visual dialog ...
Weixing Mai, Zhengxuan Zhang, Kuntao Li, Yun Xue, Fenghuan Li   +4 more
semanticscholar   +1 more source

Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs

Neural Information Processing Systems
Large Language Models (LLMs) have shown remarkable reasoning capabilities on complex tasks, but they still suffer from out-of-date knowledge, hallucinations, and opaque decision-making.
Liyi Chen, Panrong Tong, Zhongming Jin, Ying Sun, Jieping Ye, Huixia Xiong   +5 more
semanticscholar   +1 more source

Reconstructed Graph Neural Network With Knowledge Distillation for Lightweight Anomaly Detection

IEEE Transactions on Neural Networks and Learning Systems
The proliferation of Internet-of-Things (IoT) technologies in modern smart society enables massive data exchange for offering intelligent services.
Xiaokang Zhou, Jiayi Wu, Wei Liang, K. Wang, Zheng Yan, Laurence T. Yang, Qun Jin   +6 more
semanticscholar   +1 more source

GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability

arXiv.org
Improving the general capabilities of large language models (LLMs) is an active research topic. As a common data structure in many real-world domains, understanding graph data is a crucial part of advancing general intelligence. To this end, we propose a
Zihan Luo, Xiran Song, Hong Huang, Jianxun Lian, Chenhao Zhang, Jinqi Jiang, Xing Xie, Hai Jin   +7 more
semanticscholar   +1 more source

GFT: Graph Foundation Model with Transferable Tree Vocabulary

Neural Information Processing Systems
Inspired by the success of foundation models in applications such as ChatGPT, as graph data has been ubiquitous, one can envision the far-reaching impacts that can be brought by Graph Foundation Models (GFMs) with broader applications in the areas such ...
Zehong Wang, Zheyuan Zhang, Nitesh V. Chawla, Chuxu Zhang, Yanfang Ye   +4 more
semanticscholar   +1 more source

LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings

Neural Information Processing Systems
Zero-shot graph machine learning, especially with graph neural networks (GNNs), has garnered significant interest due to the challenge of scarce labeled data.
Duo Wang, Y. Zuo, Fengzhi Li, Junjie Wu
semanticscholar   +1 more source