REL: An Entity Linker Standing on the Shoulders of Giants
This provides a practical tool for retrieval systems, though it is incremental as it builds on existing neural components.
The authors tackled the lack of a modular, high-performance entity linking system by developing REL, which achieves state-of-the-art results on standard benchmarks.
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.