REL: An Entity Linker Standing on the Shoulders of Giants
- URL: http://arxiv.org/abs/2006.01969v1
- Date: Tue, 2 Jun 2020 22:51:17 GMT
- Title: REL: An Entity Linker Standing on the Shoulders of Giants
- Authors: Johannes M. van Hulst, Faegheh Hasibi, Koen Dercksen, Krisztian Balog,
Arjen P. de Vries
- Abstract summary: 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, 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.
- Score: 18.880550818914486
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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