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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Johannes M. van Hulst
- sl:arxiv_num : 2006.01969
- sl:arxiv_published : 2020-06-02T22:51:17Z
- sl:arxiv_summary : Entity linking is a standard component in modern retrieval system that is
often performed by third-party toolkits. Despite the plethora of open source
options, it is difficult to find a single system that has a modular
architecture where certain components may be replaced, does not depend on
external sources, can easily be updated to newer Wikipedia versions, and, most
important of all, has state-of-the-art performance. The REL system presented in
this paper aims to fill that gap. Building on state-of-the-art neural
components from natural language processing research, it is provided as a
Python package as well as a web API. We also report on an experimental
comparison against both well-established systems and the current
state-of-the-art on standard entity linking benchmarks.@en
- sl:arxiv_title : REL: An Entity Linker Standing on the Shoulders of Giants@en
- sl:arxiv_updated : 2020-06-02T22:51:17Z
- sl:bookmarkOf : https://arxiv.org/abs/2006.01969
- sl:creationDate : 2022-07-12
- sl:creationTime : 2022-07-12T09:16:33Z
- sl:relatedDoc : http://www.semanlink.net/doc/2022/07/2205_00820_entity_aware_trans
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