Relation adaptation: Learning to extract novel relations with minimum supervision

Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

Extracting the relations that exist between two entities is an important step in numerous Web-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 extract new 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.

Original languageEnglish
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
Pages2205-2210
Number of pages6
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia
Duration: 2011 Jul 162011 Jul 22

Other

Other22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
CityBarcelona, Catalonia
Period11/7/1611/7/22

ASJC Scopus subject areas

  • Artificial Intelligence

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