Abstract
Relation extraction is a crucial language processing task for various downstream applications, including knowledge base completion, question answering, and summarization. Traditional relation-extraction techniques, however, rely on a predefined set of relations and model the extraction as a classification task. Consequently, such closed-world extraction methods are insufficient for inducing novel relations from a corpus. Unsupervised techniques like OpenIE, which extract <head, relation, tail> triples, generate relations that are too general for practical information extraction applications. In this work, we contribute the following: 1) We motivate and introduce a new task, corpus-based task-specific relation discovery. 2) We adapt existing data sources to create Wiki-Art, a novel dataset for task-specific relation discovery. 3) We develop a novel framework for relation discovery using zero-shot entity linking, prompting, and type-specific clustering. Our approach effectively connects unstructured text spans to their shared underlying relations, bridging the data-representation gap and significantly outperforming baselines on both quantitative and qualitative metrics. Our code and data are available in our GitHub repository.- Anthology ID:
- 2023.matching-1.5
- Volume:
- Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, ON, Canada
- Editors:
- Estevam Hruschka, Tom Mitchell, Sajjadur Rahman, Dunja Mladenić, Marko Grobelnik
- Venue:
- MATCHING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 45–57
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2023.matching-1.5/
- DOI:
- 10.18653/v1/2023.matching-1.5
- Cite (ACL):
- Karthik Ramanan. 2023. Corpus-Based Task-Specific Relation Discovery. In Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023), pages 45–57, Toronto, ON, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Corpus-Based Task-Specific Relation Discovery (Ramanan, MATCHING 2023)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2023.matching-1.5.pdf