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REL: An Entity Linker Standing On The Shoulders Of Giants

Johannes M. van Hulst, Faegheh Hasibi, Koen Dercksen, Krisztian Balog, Arjen P. de Vries . Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval 2020 – 90 citations

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Compositional Generalization Content Enrichment Image Text Integration Interactive Environments Interdisciplinary Approaches Model Architecture Multimodal Semantic Representation Neural Machine Translation Productivity Enhancement Question Answering RAG SIGIR Tools

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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