Contextual Augmentation for Entity Linking using Large Language Models

📝 Original Info

• Title: Contextual Augmentation for Entity Linking using Large Language Models
• ArXiv ID: 2510.18888
• Date: 2025-10-17
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. 일반적으로 해당 연구는 자연어 처리 및 지식 그래프 분야의 전문가들(예: 대학 연구팀, 기업 AI 연구소)로 구성될 가능성이 높습니다. **

📝 Abstract

Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. Traditional methods use a two-step process with separate models for entity recognition and disambiguation, which can be computationally intensive and less effective. We propose a fine-tuned model that jointly integrates entity recognition and disambiguation in a unified framework. Furthermore, our approach leverages large language models to enrich the context of entity mentions, yielding better performance in entity disambiguation. We evaluated our approach on benchmark datasets and compared with several baselines. The evaluation results show that our approach achieves state-of-the-art performance on out-of-domain datasets.

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