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
- 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)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.coling-main.486.pdf
- Code
- markus-eberts/many-ent