---
title: "Relation Adaptation: Learning to Extract Novel Relations with Minimum Supervision"
authors:
  - "Danushka Bollegala"
  - "Yutaka Matsuo"
  - "Mitsuru Ishizuka"
doi: "10.5591/978-1-57735-516-8/IJCAI11-368"
url: "https://ijcai.org/proceedings/2011/368"
pdf: "https://ijcai.org/proceedings/2011/0368.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "2205-2209"
track: ""
keywords:
---

# Relation Adaptation: Learning to Extract Novel Relations with Minimum Supervision

Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 2205-2209. DOI: [10.5591/978-1-57735-516-8/IJCAI11-368](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-368)

## Abstract

Extracting the relations that exist between two entities is an important step in numerousWeb-related tasks such as information extraction.A supervised relation extraction system that is trained to extract a particular relation type might not accurately extract a new type of a relation for which it has not been trained.However, it is costly to create training data manually for every new relation type that one might want to extract.We propose a method to adapt an existing relation extraction system to extractnew relation types with minimum supervision. Our proposed method comprises two stages: learning a lower-dimensional projection between different relations, and learning a relational classifier for the target relation type with instance sampling. We evaluate the proposed method using a dataset that contains 2000 instances for 20 different relation types. Our experimental results show that the proposed method achieves a statistically significant macro-average F-score of 62.77. Moreover, the proposed method outperforms numerous baselines and a previously proposed weakly-supervised relation extraction method.