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