Few-shot Name Entity Recognition on StackOverflow
Xinwei Chen, Kun Li, Song, Tianyou, Jiangjian Guo · arXiv (Cornell University) · 2024
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.