While extensive studies on relation extraction have been conducted 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. In this paper, we develop a cross-lingual annotation projection method that leverages parallel corpora to bootstrap a relation detector without significant annotation efforts for a resource-poor language. In order to make our method more reliable, we introduce three simple projection noise reduction methods. The merit of our method is demonstrated through a novel Korean relation detection task.
REF propose a crosslingual annotation projection approach which uses parallel corpora to acquire a relation detector on the target language.
A Cross-lingual Annotation Projection Approach for Relation Detection
COLING
Computer Science