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Few-shot Name Entity Recognition on StackOverflow

  • arXiv (Cornell University)
  • Cornell University
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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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