Entity linking method based on Longformer and adversarial training
Jiaqi Liu, Zhanfang Zhao, Liangxun Shuo · 2024
Entity linking, the task of associating named entities mentioned in the text with their corresponding entries in a knowledge graph, addresses the inherent ambiguity in entity mentions. This process enhances semantic comprehension in both knowledge graph integration and natural language processing tasks. To address inefficiencies in processing lengthy texts and understanding complex contexts in existing models, this study proposes an innovative model, AT-LOM. The model integrates Longformer pre-training techniques and adversarial training mechanisms into text encoding, employs Extrapolatable Position Embedding (xPos) in the entity detection module and utilizes beam search algorithms in the linking module to rank text sequences generated by Long Short-Term Memory (LSTM). The AT-LOM model demonstrates significant performance enhancements on the standard AIDA-CoNLL dataset, achieving a 1.0% to 13.1% increase in Micro-F1 values compared to mainstream methods in recent years.