Abstract
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.
Recommended Citation
S. Chatterjee and L. Dietz, "Predicting Guiding Entities for Entity Aspect Linking," International Conference on Information and Knowledge Management, Proceedings, pp. 3848 - 3852, Association for Computing Machinery, Oct 2022.
The definitive version is available at https://doi.org/10.1145/3511808.3557671
Department(s)
Computer Science
Publication Status
Public Access
Keywords and Phrases
document similarity; entity aspect linking; entity ranking
International Standard Book Number (ISBN)
978-145039236-5
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery, All rights reserved.
Publication Date
17 Oct 2022
Comments
Directorate for Computer and Information Science and Engineering, Grant 1846017