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22 February 2023 A simple semantic ranking approach for entity linking
Tingting Wei, Aimin Yang, Sunjie Huang, Shiling Song
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Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 125870L (2023) https://doi.org/10.1117/12.2667421
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China
Abstract
Entity Linking is the task of mapping entity mentions in text to real entity in a given knowledge base. One of the main obstacles to this task is the variations of name and the ambiguity of the entity. Prior studies have explored various methods for modeling local semantic relatedness between each entity-mention pair and global topical mention consistency to address these issues. Nevertheless, most of these existing methods are relatively complex so that they are impractical in some situation, e.g. on-line search-engine. This paper proposes a simple approach that combines the merits of the local and global paradigm, which aims at simplifying the entity linking process and achieving considerable performance. This proposed approach benefits from a semantic relatedness measure for local modeling and a collective score for global coherence guarantees. Our approach is evaluated on different types of datasets and the results show that, despite its simplicity, our model can achieve comparable performance compared to other similar metrics.
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Tingting Wei, Aimin Yang, Sunjie Huang, and Shiling Song "A simple semantic ranking approach for entity linking", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 125870L (22 February 2023); https://doi.org/10.1117/12.2667421
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KEYWORDS
Semantics
Machine learning
Data modeling
Performance modeling
Modeling
Neural networks
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