  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.
