ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages
Colin Lockard, Prashant Shiralkar, Xin Luna Dong, Hannaneh Hajishirzi
Abstract
In many documents, such as semi-structured webpages, textual semantics are augmented with additional information conveyed using visual elements including layout, font size, and color. Prior work on information extraction from semi-structured websites has required learning an extraction model specific to a given template via either manually labeled or distantly supervised data from that template. In this work, we propose a solution for “zero-shot” open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals. Our model uses a graph neural network-based approach to build a rich representation of text fields on a webpage and the relationships between them, enabling generalization to new templates. Experiments show this approach provides a 31% F1 gain over a baseline for zero-shot extraction in a new subject vertical.- Anthology ID:
- 2020.acl-main.721
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8105–8117
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.721
- DOI:
- 10.18653/v1/2020.acl-main.721
- Cite (ACL):
- Colin Lockard, Prashant Shiralkar, Xin Luna Dong, and Hannaneh Hajishirzi. 2020. ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8105–8117, Online. Association for Computational Linguistics.
- Cite (Informal):
- ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages (Lockard et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.acl-main.721.pdf