156 citations · 342 across the 14 of their papers we have counts for
14 papers
Variational Multi-Modal Hypergraph Attention Network for Multi-Modal Relation Extraction
Qian Li, Cheng Ji, Shu Guo +5
Multi-modal relation extraction (MMRE) is a challenging task that aims to identify relations between entities in text leveraging image information. Existing methods are limited by…
Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion
Qian Li, Shu Guo, Yinjia Chen +3
Few-shot knowledge graph completion (FKGC) aims to query the unseen facts of a relation given its few-shot reference entity pairs. The side effect of noises due to the uncertainty…
Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment
Qian Li, Cheng Ji, Shu Guo +3
Multi-Modal Entity Alignment (MMEA) is a critical task that aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs). However, this task faces challenge…
Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction
Qian Li, Shu Guo, Cheng Ji +3
Multi-Modal Relation Extraction (MMRE) aims at identifying the relation between two entities in texts that contain visual clues. Rich visual content is valuable for the MMRE task,…
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment
Qian Li, Shu Guo, Yangyifei Luo +4
The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable…
A Comprehensive Survey on Automatic Knowledge Graph Construction
Lingfeng Zhong, Jia Wu, Qian Li +2
Automatic knowledge graph construction aims to manufacture structured human knowledge. To this end, much effort has historically been spent extracting informative fact patterns fro…
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
Ce Zhou, Qian Li, Chen Li +16
Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A PFM (e.g., BERT, ChatGPT, and GPT-4) is trained on…
Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems
Qian Li, Jianxin Li, Lihong Wang +6
Event detection in power systems aims to identify triggers and event types, which helps relevant personnel respond to emergencies promptly and facilitates the optimization of power…
Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing
Qingyun Sun, Jianxin Li, Haonan Yuan +5
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs. What topolog…
Event Extraction by Associating Event Types and Argument Roles
Qian Li, Shu Guo, Jia Wu +5
Event extraction (EE), which acquires structural event knowledge from texts, can be divided into two sub-tasks: event type classification and element extraction (namely identifying…
CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction
Jiawei Sheng, Shu Guo, Bowen Yu +5
Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. Most existing methods assume that events appear in sentences without…
A Survey on Deep Learning Event Extraction: Approaches and Applications
Qian Li, Jianxin Li, Jiawei Sheng +8
Event extraction (EE) is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, EE based on dee…
Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations
Qian Li, Hao Peng, Jianxin Li +5
Event extraction is a fundamental task for natural language processing. Finding the roles of event arguments like event participants is essential for event extraction. However, doi…
A Survey on Text Classification: From Shallow to Deep Learning
Qian Li, Hao Peng, Jianxin Li +5
Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented su…