mlsanityData to 7 Oct 2026

Few-shot Name Entity Recognition on StackOverflow

arXiv 2404.09405 ↗First posted 15 Apr 2024cs.CL cs.AIcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 15 Apr 2024.

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11 of 18 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2021Prompt-Learning for Fine-Grained Entity Typingcited by 62
  5. 2023Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networkscited by 54
  6. 2018Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagerycited by 42
  7. 2020Code and Named Entity Recognition in StackOverflowcited by 39
  8. 2022Decomposed Meta-Learning for Few-Shot Named Entity Recognitioncited by 22
  9. 2023Influence Pathway Discovery on Social Mediacited by 11
  10. 2022Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance Generationcited by 9
  11. 2019Question Relatedness on Stack Overflow: The Task, Dataset, and Corpus-inspired Modelscited by 4

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Citing papers per year: 2024: 11; 2025: 3.

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