基于对比学习和重排序的实体链接方法研究OA

Entity Linking(EL),which links entity mentions in natural text to corresponding entities within a knowledge base,plays a crucial role in fields such as information retrieval and question-answering systems.The key challenge in EL lies in the effective utilization of the contextual information of mentions and feature information of entities in the knowledge base to generate candidate entities and select the correct entity among them.Although some methods generate relevant candidate entities by relying on specific strategies and select appropriate entities by combining feature information,they often fail to capture deeper semantic relationships,resulting in an inability to obtain high-quality candidate entities.In some cases,the correct entity may not be included among the candidate entities.Meanwhile,in specific domains,limited entity information may impair the capability of some methods for multi-level interaction.To address these issues,this paper proposes a two-stage EL method.This method first generates high-quality candidate entities and then aggregates entity feature information to achieve re-ranking at both coarse-and fine-grained levels.Specifically,it retrieves high-quality candidate entities using a contrastive learning method based on hybrid negative sample sampling.Subsequently,it predicts the fine-grained entity types in a weakly supervised manner and uses both coarse-and fine-grained type information to re-rank the candidate entities.Experimental results on three public datasets demonstrate the effectiveness of this method in enhancing EL performance.