Few-shot Name Entity Recognition on StackOverflow
This addresses annotation challenges for developers or researchers working with StackOverflow data, though it appears incremental.
The paper tackled the challenge of named entity recognition on StackOverflow with limited labeled data by proposing a RoBERTa+MAML meta-learning method, achieving a 5% F1 score improvement over the baseline on a corpus with 27 entity types.
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.