REL: An Entity Linker Standing on the Shoulders of Giants

By Johannes M. van Hulst, Faegheh Hasibi, Koen Dercksen, Krisztian Balog, A. D. Vries. 2020

152 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research, Natural Language Processing, Information Extraction

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Citations in the corpus

Robustness Gym: Unifying the NLP Evaluation Landscape (2021, background): “…compare them to 3 state-5 of-the-art systems and a heuristic baseline: (i) B OOTLEG [Orr et al., 2020], a self-supervised system for NEL, (ii) R EL [van Hulst et al., 2020], a system that combines existing state-of-the-art approaches, (iii) W AT [Piccinno and Ferragina, 2014] an extension of the…”