Paper: Self-Annotation for fine-grained geospatial relation extraction

ACL ID C10-1010
Title Self-Annotation for fine-grained geospatial relation extraction
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010

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 intro- duce self-annotation as a new supervised learning approach for developing and im- plementing a system that extracts fine- grained relations between entities. The main benefit of self-annotation is that it does not require manual labeling. The in- put of the learned model is a represen- tation of the free text, its output struc- tured relations. Thus, the model, once learned, can be applied to any arbitrary free text. We describe the challenges for the self-annota...