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 (translated)
StackOverflow作为一个庞大的问题库，其有限的带标签示例，对我们提出了一个注释挑战。为了应对这个空白，我们提出了RoBERTa+MAML，一种利用元学习技术的几 shot 命名实体识别（NER）方法。我们的方法在StackOverflow NER数据集（27个实体类型）上评估，与基线相比，实现了5%的F1分数提高。我们还在领域特定的短语处理和增强结果方面进一步提高了结果。
URL
https://arxiv.org/abs/2404.09405
