ManyEnt: A Dataset for Few-shot Entity Typing

Markus Eberts, Kevin Pech, Adrian Ulges
2020 Proceedings of the 28th International Conference on Computational Linguistics   unpublished
We introduce ManyEnt, a benchmark for entity typing models in few-shot scenarios. ManyEnt offers a rich typeset, with a fine-grain variant featuring 256 entity types and a coarse-grain one with 53 entity types. Both versions have been derived from the Wikidata knowledge graph in a semi-automatic fashion. We also report results for two baselines using BERT, reaching up to 70.68% accuracy (10-way 1-shot).
doi:10.18653/v1/2020.coling-main.486 fatcat:g4he6rqgo5fvnmy6doandmruse