MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations

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Main Authors: Retyk, Federico, Gasco, Luis, Carrino, Casimiro Pio, Deniz, Daniel, Zbib, Rabih
Format: Preprint
Published: 2024
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author Retyk, Federico
Gasco, Luis
Carrino, Casimiro Pio
Deniz, Daniel
Zbib, Rabih
author_facet Retyk, Federico
Gasco, Luis
Carrino, Casimiro Pio
Deniz, Daniel
Zbib, Rabih
contents We present the Multilingual Entity Linking of Occupations (MELO) Benchmark, a new collection of 48 datasets for evaluating the linking of entity mentions in 21 languages to the ESCO Occupations multilingual taxonomy. MELO was built using high-quality, pre-existent human annotations. We conduct experiments with simple lexical models and general-purpose sentence encoders, evaluated as bi-encoders in a zero-shot setup, to establish baselines for future research. The datasets and source code for standardized evaluation are publicly available at https://github.com/Avature/melo-benchmark
format Preprint
id arxiv_https___arxiv_org_abs_2410_08319
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations
Retyk, Federico
Gasco, Luis
Carrino, Casimiro Pio
Deniz, Daniel
Zbib, Rabih
Computation and Language
We present the Multilingual Entity Linking of Occupations (MELO) Benchmark, a new collection of 48 datasets for evaluating the linking of entity mentions in 21 languages to the ESCO Occupations multilingual taxonomy. MELO was built using high-quality, pre-existent human annotations. We conduct experiments with simple lexical models and general-purpose sentence encoders, evaluated as bi-encoders in a zero-shot setup, to establish baselines for future research. The datasets and source code for standardized evaluation are publicly available at https://github.com/Avature/melo-benchmark
title MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations
topic Computation and Language
url https://arxiv.org/abs/2410.08319