Abstract
Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation. Learning the implicit structured representations of entity names without context and external knowledge is particularly challenging. In this paper, we present a novel learning framework that combines active learning and weak supervision to solve this problem. Our experimental evaluation show that this framework enables the learning of high-quality models from merely a dozen or so labeled examples.- Anthology ID:
- 2020.emnlp-main.517
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6376–6383
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.517
- DOI:
- 10.18653/v1/2020.emnlp-main.517
- Cite (ACL):
- Kun Qian, Poornima Chozhiyath Raman, Yunyao Li, and Lucian Popa. 2020. Learning Structured Representations of Entity Names using Active Learning and Weak Supervision. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6376–6383, Online. Association for Computational Linguistics.
- Cite (Informal):
- Learning Structured Representations of Entity Names using Active Learning and Weak Supervision (Qian et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2020.emnlp-main.517.pdf
- Code
- System-T/PARTNER