Summary of Few-shot Name Entity Recognition on Stackoverflow, by Xinwei Chen et al.
Few-shot Name Entity Recognition on StackOverflow
by Xinwei Chen, Kun Li, Tianyou Song, Jiangjian Guo
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes RoBERTa+MAML, a few-shot named entity recognition (NER) method that leverages meta-learning to address the annotation challenge on StackOverflow, a vast question repository with limited labeled examples. The approach achieves a 5% F1 score improvement over the baseline on the StackOverflow NER corpus, which consists of 27 entity types. By processing domain-specific phrases, the results are further improved. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RoBERTa+MAML is a new method for named entity recognition that uses meta-learning to work well even with limited training data. This is important because StackOverflow has many questions, but very few of them have labels, making it hard to train machines to recognize entities in text. The researchers tried their method on the StackOverflow NER corpus and got better results than before. |
Keywords
» Artificial intelligence » F1 score » Few shot » Meta learning » Named entity recognition » Ner
