Few-shot Name Entity Recognition on StackOverflow
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913333027799040 |
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| author | Chen, Xinwei Li, Kun Song, Tianyou Guo, Jiangjian |
| author_facet | Chen, Xinwei Li, Kun Song, Tianyou Guo, Jiangjian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09405 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Few-shot Name Entity Recognition on StackOverflow Chen, Xinwei Li, Kun Song, Tianyou Guo, Jiangjian Computation and Language Artificial Intelligence 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. |
| title | Few-shot Name Entity Recognition on StackOverflow |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2404.09405 |