Fine-Grained Entity Typing with High-Multiplicity Assignments

Maxim Rabinovich, Dan Klein


Abstract
As entity type systems become richer and more fine-grained, we expect the number of types assigned to a given entity to increase. However, most fine-grained typing work has focused on datasets that exhibit a low degree of type multiplicity. In this paper, we consider the high-multiplicity regime inherent in data sources such as Wikipedia that have semi-open type systems. We introduce a set-prediction approach to this problem and show that our model outperforms unstructured baselines on a new Wikipedia-based fine-grained typing corpus.
Anthology ID:
P17-2052
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
330–334
Language:
URL:
https://aclanthology.org/P17-2052
DOI:
10.18653/v1/P17-2052
Bibkey:
Cite (ACL):
Maxim Rabinovich and Dan Klein. 2017. Fine-Grained Entity Typing with High-Multiplicity Assignments. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 330–334, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Fine-Grained Entity Typing with High-Multiplicity Assignments (Rabinovich & Klein, ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-2052.pdf