Abstract
The advent of neural-networks in NLP brought with it substantial improvements in supervised relation extraction. However, obtaining a sufficient quantity of training data remains a key challenge. In this work we propose a process for bootstrapping training datasets which can be performed quickly by non-NLP-experts. We take advantage of search engines over syntactic-graphs (Such as Shlain et al. (2020)) which expose a friendly by-example syntax. We use these to obtain positive examples by searching for sentences that are syntactically similar to user input examples. We apply this technique to relations from TACRED and DocRED and show that the resulting models are competitive with models trained on manually annotated data and on data obtained from distant supervision. The models also outperform models trained using NLG data augmentation techniques. Extending the search-based approach with the NLG method further improves the results.- Anthology ID:
- 2021.eacl-main.128
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1491–1503
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.128
- DOI:
- 10.18653/v1/2021.eacl-main.128
- Cite (ACL):
- Matan Eyal, Asaf Amrami, Hillel Taub-Tabib, and Yoav Goldberg. 2021. Bootstrapping Relation Extractors using Syntactic Search by Examples. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1491–1503, Online. Association for Computational Linguistics.
- Cite (Informal):
- Bootstrapping Relation Extractors using Syntactic Search by Examples (Eyal et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.eacl-main.128.pdf
- Code
- mataney/BootstrappingRelationExtractors
- Data
- DocRED