110 citations · 242 across the 22 of their papers we have counts for
8 papers · 1 filter
Bipartite Graph Embedding via Mutual Information Maximization
Jiangxia Cao, Xixun Lin, Shu Guo +3
Bipartite graph embedding has recently attracted much attention due to the fact that bipartite graphs are widely used in various application domains. Most previous methods, which a…
Label Enhanced Event Detection with Heterogeneous Graph Attention Networks
Shiyao Cui, Bowen Yu, Xin Cong +3
Event Detection (ED) aims to recognize instances of specified types of event triggers in text. Different from English ED, Chinese ED suffers from the problem of word-trigger mismat…
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field
Xin Cong, Shiyao Cui, Bowen Yu +3
Event detection tends to struggle when it needs to recognize novel event types with a few samples. The previous work attempts to solve this problem in the identify-then-classify ma…
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking
Yucheng Wang, Bowen Yu, Yueyang Zhang +3
Extracting entities and relations from unstructured text has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in identifying…
Adaptive Attentional Network for Few-Shot Knowledge Graph Completion
Jiawei Sheng, Shu Guo, Zhenyu Chen +4
Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs. Rece…
Coarse-to-Fine Pre-training for Named Entity Recognition
Mengge Xue, Bowen Yu, Zhenyu Zhang +3
More recently, Named Entity Recognition hasachieved great advances aided by pre-trainingapproaches such as BERT. However, currentpre-training techniques focus on building lan-guage…
Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering
Xin Cong, Bowen Yu, Tingwen Liu +3
Few-shot classification tends to struggle when it needs to adapt to diverse domains. Due to the non-overlapping label space between domains, the performance of conventional domain…
Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation
Shiyao Cui, Bowen Yu, Tingwen Liu +3
Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific event types in text. Previous studies on the task have verified the effective…