Few-shot Name Entity Recognition on StackOverflow

arXiv:2404.09405v1 Announce Type: new
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.

abstract annotation arxiv challenge cs.ai cs.cl examples few-shot gap improvement meta meta-learning ner question raise recognition roberta stackoverflow type types vast