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
@article{arxiv.2410.08319,
title = {MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations},
author = {Federico Retyk and Luis Gasco and Casimiro Pio Carrino and Daniel Deniz and Rabih Zbib},
journal= {arXiv preprint arXiv:2410.08319},
year = {2024}
}
Comments
Accepted to the 4th Workshop on Recommender Systems for Human Resources (RecSys in HR 2024) as part of RecSys 2024