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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/P17-2052.pdf