Predicting Guiding Entities for Entity Aspect Linking
Predicting Guiding Entities for Entity Aspect Linking
复制标题
DOI：
10.1145/3511808.3557671
复制
发表时间：
2022-10
期刊：
影响因子：
--
通讯作者：
Shubham Chatterjee;Laura Dietz
中科院分区：
文献类型：
--
作者：
Shubham Chatterjee;Laura Dietz
实体链接可以消除文本中实体的歧义。然而，一个实体有许多不同的方面可以讨论，但不能通过实体联系加以区分，例如，在“食品”或“生态系统”的背景下，实体“牡蛎”。实体方面链接通过标识给定上下文中实体的最相关方面来为实体链接提供这种细粒度的显式语义。我们提出了一种新的实体方面链接的方法，优于几个神经和非神经基线上的大规模实体方面链接测试集合。我们的方法使用一个有监督的神经实体排名系统来预测上下文的相关实体。然后，这些实体用于将系统引导到正确的方面。
Entity linking can disambiguate mentions of an entity in text. However, there are many different aspects of an entity that could be discussed but are not differentiable by entity links, for example, the entity "oyster'' in the context of "food'' or "ecosystems''. Entity aspect linking provides such fine-grained explicit semantics for entity links by identifying the most relevant aspect of an entity in the given context. We propose a novel entity aspect linking approach that outperforms several neural and non-neural baselines on a large-scale entity aspect linking test collection. Our approach uses a supervised neural entity ranking system to predict relevant entities for the context. These entities are then used to guide the system to the correct aspect.