Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction
Abstract
This paper addresses the tasks of automatic seed selection for bootstrapping relation extraction, and noise reduction for distantly supervised relation extraction. We first point out that these tasks are related. Then, inspired by ranking relation instances and patterns computed by the HITS algorithm, and selecting cluster centroids using the K-means, LSA, or NMF method, we propose methods for selecting the initial seeds from an existing resource, or reducing the level of noise in the distantly labeled data. Experiments show that our proposed methods achieve a better performance than the baseline systems in both tasks.- Anthology ID:
- P18-2015
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 89–95
- Language:
- URL:
- https://aclanthology.org/P18-2015
- DOI:
- 10.18653/v1/P18-2015
- Cite (ACL):
- Van-Thuy Phi, Joan Santoso, Masashi Shimbo, and Yuji Matsumoto. 2018. Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 89–95, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction (Phi et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/P18-2015.pdf
- Code
- pvthuy/part-whole-relations
- Data
- Part Whole Relations