From actors, politicians, to CEOs: domain adaptation of relational extractors using a latent relational mapping

  • Authors:

  • Danushka Bollegala;Yutaka Matsuo;Mitsuru Ishizuka

  • Affiliations:

  • The University of Tokyo, Tokyo, Japan;The University of Tokyo, Tokyo, Japan;The University of Tokyo, Tokyo, Japan

  • Venue:
  • Proceedings of the 20th international conference companion on World wide web
  • Year:
  • 2011

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Abstract

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.