@inproceedings{kang-etal-2024-guidance,
title = "Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition",
author = "Kang, Hyeonseok and
Seo, Hyein and
Jung, Jeesu and
Jung, Sangkeun and
Chang, Du-Seong and
Chung, Riwoo",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-short.61",
pages = "665--672",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition
%A Kang, Hyeonseok
%A Seo, Hyein
%A Jung, Jeesu
%A Jung, Sangkeun
%A Chang, Du-Seong
%A Chung, Riwoo
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F kang-etal-2024-guidance
%X 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.
%U https://aclanthology.org/2024.acl-short.61
%P 665-672
Markdown (Informal)
[Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition](https://aclanthology.org/2024.acl-short.61) (Kang et al., ACL 2024)
ACL