Introducing high correlation and high quality instances for few-shot entity linking.

Area of Science:

• Natural Language Processing
• Information Extraction

Background:

• Entity linking is crucial for NLP tasks but suffers from data scarcity in specialized domains.
• Existing methods using synthetic data often introduce noise, hindering model performance.
• Few-shot entity linking is vital for real-world applications with limited labeled data.

Purpose of the Study:

• To propose a novel framework (H²FEL) for high-quality, high-correlation instance generation for few-shot entity linking.
• To overcome the limitations of low-quality synthetic data in specialized domains.
• To improve the semantic understanding of entity linking models.

Main Methods:

• Developed an adversarial instance extraction module to identify high-correlation instances from general domains.
• Employed a curriculum learning variant to train the entity linking model, mitigating noise from low-correlation instances.
• Focused on few-shot learning scenarios to address data scarcity.

Main Results:

• The H²FEL framework effectively introduces high-quality and high-correlation instances.
• Experimental results demonstrate significant improvements in few-shot entity linking performance.
• Achieved state-of-the-art results on the few-shot entity linking dataset.

Conclusions:

• The proposed H²FEL framework offers a robust solution for few-shot entity linking.
• High-quality, relevant instance generation is key to overcoming data scarcity challenges.
• The method shows strong potential for practical applications in specialized domains.