Abstract
For the task of relation extraction, distant supervision is an efficient approach to generate labeled data by aligning knowledge base with free texts. The essence of it is a challenging incomplete multi-label classification problem with sparse and noisy features. To address the challenge, this work presents a novel nonparametric Bayesian formulation for the task. Experiment results show substantially higher top precision improvements over the traditional state-of-the-art approaches.- Anthology ID:
- D17-1192
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1808–1813
- Language:
- URL:
- https://aclanthology.org/D17-1192
- DOI:
- 10.18653/v1/D17-1192
- Cite (ACL):
- Qing Zhang and Houfeng Wang. 2017. Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1808–1813, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective (Zhang & Wang, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/D17-1192.pdf