Few-shot Name Entity Recognition on StackOverflow
Chen, Xinwei, Li, Kun, Song, Tianyou, Guo, Jiangjian
–arXiv.org Artificial Intelligence
StackOverflow, with its vast question repository and limited labeled examples, raise an annotation challenge for us. We address this gap by proposing RoBERTa+MAML, a few-shot named entity recognition (NER) method leveraging meta-learning. Our approach, evaluated on the StackOverflow NER corpus (27 entity types), achieves a 5% F1 score improvement over the baseline. We improved the results further domain-specific phrase processing enhance results.
arXiv.org Artificial Intelligence
Apr-27-2024
- Country:
- North America > United States
- California (0.14)
- New York (0.14)
- North America > United States
- Genre:
- Research Report (0.51)
- Technology: