Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866915776308445184 |
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| author | Kang, Hyeonseok Seo, Hyein Jung, Jeesu Jung, Sangkeun Chang, Du-Seong Chung, Riwoo |
| author_facet | Kang, Hyeonseok Seo, Hyein Jung, Jeesu Jung, Sangkeun Chang, Du-Seong Chung, Riwoo |
| contents | While the abundance of rich and vast datasets across numerous fields has facilitated the advancement of natural language processing, sectors in need of specialized data types continue to struggle with the challenge of finding quality data. Our study introduces a novel guidance data augmentation technique utilizing abstracted context and sentence structures to produce varied sentences while maintaining context-entity relationships, addressing data scarcity challenges. By fostering a closer relationship between context, sentence structure, and role of entities, our method enhances data augmentation's effectiveness. Consequently, by showcasing diversification in both entity-related vocabulary and overall sentence structure, and simultaneously improving the training performance of named entity recognition task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18442 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition Kang, Hyeonseok Seo, Hyein Jung, Jeesu Jung, Sangkeun Chang, Du-Seong Chung, Riwoo Computation and Language While the abundance of rich and vast datasets across numerous fields has facilitated the advancement of natural language processing, sectors in need of specialized data types continue to struggle with the challenge of finding quality data. Our study introduces a novel guidance data augmentation technique utilizing abstracted context and sentence structures to produce varied sentences while maintaining context-entity relationships, addressing data scarcity challenges. By fostering a closer relationship between context, sentence structure, and role of entities, our method enhances data augmentation's effectiveness. Consequently, by showcasing diversification in both entity-related vocabulary and overall sentence structure, and simultaneously improving the training performance of named entity recognition task. |
| title | Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.18442 |