Abstract
Entity linking refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB). It can bridge the gap between unstructured nature language documents that computers hardly understand and a structured semantic Knowledge Base which can be easily processed by computers. Existing studies and systems about entity linking mainly focus on the open domain, which may cause three problems: 1. linking to unconcerned entities; 2. time and space consuming; 3. less precision. In this paper, we address the problem by restricting entity linking into specific domains and leveraging domain information to enhance the linking performance. We propose an unsupervised method to generate domain data from Wikipedia and provide a domain-specific neural collective entity linking model for each domain. Based on domain data and domain models, we build a system that can provide domain entity linking for users. Our system, Domain-Specific neural collective Entity Linking system (DSEL), supporting entity linking in 12 domains, is published as an online website, https://dsel.xlore.org.
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Acknowledgments
The work is supported by NSFC key projects (U1736204, 61533018, 61661146007), Key Technology Develop and Research Project of SGCC (5400-201953257A-0-0-00), Ministry of Education and China Mobile Joint Fund (MCM20170301), and THUNUS NExT Co-Lab.
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Zhang, X. et al. (2020). DSEL: A Domain-Specific Entity Linking System. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Lecture Notes in Computer Science(), vol 12032. Springer, Cham. https://doi.org/10.1007/978-3-030-41407-8_10
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DOI: https://doi.org/10.1007/978-3-030-41407-8_10
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