Abstract

Relation Extraction (RE) is the task of extracting semantic relationships between entities in text. Recent studies on rela-tion extraction are mostly supervised. The clear drawback of supervised methods is the need of training data: labeled data is expensive to obtain, and there is often a mismatch between the training data and the data the system will be applied to. This is the problem of domain adapta-tion. In this paper, we propose to combine (i) term generalization approaches such as word clustering and latent semantic anal-ysis (LSA) and (ii) structured kernels to improve the adaptability of relation ex-tractors to new text genres/domains. The empirical evaluation on ACE 2005 do-mains shows that a suitable combination of syntax and lexical generalization is very promising for domain adaptation. 1