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Although researchers have conducted extensive studies on relation extraction in the last decade, statistical systems based on supervised learning are still limited, because they require large amounts of training data to achieve high performance level. In this article, we propose cross-lingual annotation projection methods that leverage parallel corpora to build a relation extraction system for a resource-poor language without significant annotation efforts. To make our method more reliable, we introduce two types of projection approaches with noise reduction strategies. We demonstrate the merit of our method using a Korean relation extraction system trained on projected examples from an English-Korean parallel corpus. Experiments show the feasibility of our approaches through comparison to other systems based on monolingual resources.