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A great deal of information on the Web is represented in both textual and structured form. The structured form is machine-readable and can be used to augment the textual data. We call this augmentation - the annotation of texts with relations that are included in the structured data - self-annotation. In this paper, we introduce self-annotation as a new supervised learning approach for developing and implementing a system that extracts fine-grained relations between entities. The main benefit of self-annotation is that it does not require manual labeling. The input of the learned model is a representation of the free text, its output structured relations. Thus, the model, once learned, can be applied to any arbitrary free text. We describe the challenges for the self-annotation process and give results for a sample relation extraction system. To deal with the challenge of fine-grained relations, we implement and evaluate both shallow and deep linguistic analysis, focusing on German.