CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12587 > Article
Paper
22 February 2023 A simple semantic ranking approach for entity linking
Tingting Wei, Aimin Yang, Sunjie Huang, Shiling Song
Author Affiliations +
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.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
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
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 9 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Semantics
Machine learning
Data modeling
Performance modeling
Modeling
Neural networks
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top