150 citations · 375 across the 20 of their papers we have counts for
7 papers · 1 filter
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification
Juan Li, Ruoxu Wang, Ningyu Zhang +3
Relation classification aims to extract semantic relations between entity pairs from the sentences. However, most existing methods can only identify seen relation classes that occu…
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction
Haiyang Yu, Ningyu Zhang, Shumin Deng +3
Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings. However, people can gras…
FedE: Embedding Knowledge Graphs in Federated Setting
Mingyang Chen, Wen Zhang, Zonggang Yuan +2
Knowledge graphs (KGs) consisting of triples are always incomplete, so it's important to do Knowledge Graph Completion (KGC) by predicting missing triples. Multi-Source KG is a com…
Contrastive Triple Extraction with Generative Transformer
Hongbin Ye, Ningyu Zhang, Shumin Deng +4
Triple extraction is an essential task in information extraction for natural language processing and knowledge graph construction. In this paper, we revisit the end-to-end triple e…
Neural Entity Summarization with Joint Encoding and Weak Supervision
Junyou Li, Gong Cheng, Qingxia Liu +4
In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts. Many applications require abridged versions of entity descriptions…
Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection
Shumin Deng, Ningyu Zhang, Jiaojian Kang +3
Event detection (ED), a sub-task of event extraction, involves identifying triggers and categorizing event mentions. Existing methods primarily rely upon supervised learning and re…
Relation Adversarial Network for Low Resource Knowledge Graph Completion
Ningyu Zhang, Shumin Deng, Zhanlin Sun +3
Knowledge Graph Completion (KGC) has been proposed to improve Knowledge Graphs by filling in missing connections via link prediction or relation extraction. One of the main difficu…