REL: An Entity Linker Standing on the Shoulders of Giants

TL;DR

REL is a modular, open-source entity linking system that leverages neural NLP components, offering easy updates, independence from external sources, and competitive performance on standard benchmarks.

Contribution

The paper introduces REL, a flexible, state-of-the-art entity linker built with neural components, available as a Python package and web API.

Findings

REL achieves competitive results on standard benchmarks.

REL's modular design allows easy component replacement and updates.

REL does not depend on external sources for entity linking.

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.