Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition
It addresses data scarcity for named entity recognition in specialized domains, but appears incremental as it builds on existing data augmentation methods.
The paper tackled data scarcity in specialized domains for named entity recognition by introducing a guidance-based prompt data augmentation technique that maintains context-entity relationships, resulting in improved training performance and diversification in entity vocabulary and sentence structure.
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.