16 citations · 25 across the 8 of their papers we have counts for
7 papers · 1 filter
VicunaNER: Zero/Few-shot Named Entity Recognition using Vicuna
Bin Ji
Large Language Models (LLMs, e.g., ChatGPT) have shown impressive zero- and few-shot capabilities in Named Entity Recognition (NER). However, these models can only be accessed via…
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…
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…
A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search
Huijun Liu, Jie Yu, Shasha Li +2
Textual adversarial attacks expose the vulnerabilities of text classifiers and can be used to improve their robustness. Existing context-aware methods solely consider the gold labe…
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…
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…