  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.
