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.
['Danushka Bollegala', 'Yutaka Matsuo', 'Mitsuru Ishizuka']
From actors, politicians, to CEOs: domain adaptation of relational extractors using a latent relational mapping