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Few-shot Name Entity Recognition on StackOverflow
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Abstract
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.
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Publication details
- DOI
- 10.48550/arxiv.2404.09405
- OpenAlex
- W4394866560
- Document type
- preprint
- Language
- EN
- Source
- arXiv (Cornell University)
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