most citedFew-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes

16 citations · 17 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2023

Physically Adversarial Infrared Patches with Learnable Shapes and Locations

Wei Xingxing, Yu Jie, Huang Yao

Owing to the extensive application of infrared object detectors in the safety-critical tasks, it is necessary to evaluate their robustness against adversarial examples in the real…

cs.CL2023

Dynamic Multi-View Fusion Mechanism For Chinese Relation Extraction

Jing Yang, Bin Ji, Shasha Li +3

Recently, many studies incorporate external knowledge into character-level feature based models to improve the performance of Chinese relation extraction. However, these methods te…

cs.CL2022

A Two-Phase Paradigm for Joint Entity-Relation Extraction

Bin Ji, Hao Xu, Jie Yu +4

An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task. However, these models sample a large number of negative e…

cs.CL202216 cited

Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes

Bin Ji, Shasha Li, Shaoduo Gan +3

Few-shot named entity recognition (NER) enables us to build a NER system for a new domain using very few labeled examples. However, existing prototypical networks for this task suf…

cs.CL20221 cited

Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition

Bin Ji, Shasha Li, Jie Yu +2

For Named Entity Recognition (NER), sequence labeling-based and span-based paradigms are quite different. Previous research has demonstrated that the two paradigms have clear compl…

cs.CL2022

SummScore: A Comprehensive Evaluation Metric for Summary Quality Based on Cross-Encoder

Wuhang Lin, Shasha Li, Chen Zhang +4

Text summarization models are often trained to produce summaries that meet human quality requirements. However, the existing evaluation metrics for summary text are only rough prox…