ManyEnt: A Dataset for Few-shot Entity Typing

Markus Eberts, Kevin Pech, Adrian Ulges


Abstract
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).
Anthology ID:
2020.coling-main.486
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5553–5557
Language:
URL:
https://aclanthology.org/2020.coling-main.486
DOI:
10.18653/v1/2020.coling-main.486
Bibkey:
Cite (ACL):
Markus Eberts, Kevin Pech, and Adrian Ulges. 2020. ManyEnt: A Dataset for Few-shot Entity Typing. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5553–5557, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
ManyEnt: A Dataset for Few-shot Entity Typing (Eberts et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.486.pdf
Code
 markus-eberts/many-ent