Weakly Supervised Named Entity Tagging with Learnable Logical Rules
Jiacheng Li, Haibo Ding, Jingbo Shang, Julian McAuley, Zhe Feng
Abstract
We study the problem of building entity tagging systems by using a few rules as weak supervision. Previous methods mostly focus on disambiguating entity types based on contexts and expert-provided rules, while assuming entity spans are given. In this work, we propose a novel method TALLOR that bootstraps high-quality logical rules to train a neural tagger in a fully automated manner. Specifically, we introduce compound rules that are composed from simple rules to increase the precision of boundary detection and generate more diverse pseudo labels. We further design a dynamic label selection strategy to ensure pseudo label quality and therefore avoid overfitting the neural tagger. Experiments on three datasets demonstrate that our method outperforms other weakly supervised methods and even rivals a state-of-the-art distantly supervised tagger with a lexicon of over 2,000 terms when starting from only 20 simple rules. Our method can serve as a tool for rapidly building taggers in emerging domains and tasks. Case studies show that learned rules can potentially explain the predicted entities.- Anthology ID:
- 2021.acl-long.352
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4568–4581
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.352
- DOI:
- 10.18653/v1/2021.acl-long.352
- Cite (ACL):
- Jiacheng Li, Haibo Ding, Jingbo Shang, Julian McAuley, and Zhe Feng. 2021. Weakly Supervised Named Entity Tagging with Learnable Logical Rules. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4568–4581, Online. Association for Computational Linguistics.
- Cite (Informal):
- Weakly Supervised Named Entity Tagging with Learnable Logical Rules (Li et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.acl-long.352.pdf
- Code
- JiachengLi1995/TALLOR + additional community code
- Data
- BC5CDR, CoNLL-2003