Local Adaptive Extraction of References

Abstract. The accurate extraction of scholarly reference information from scientific publications is essential for many useful applications like BIBTEX management systems or citation analysis. Automatic extraction methods suffer from the heterogeneity of reference notation, no matter wether the extraction model was handcrafted or learnt from labeled data. However, references of the same paper or journal are usually homogeneous. We exploit this local consistency with a novel approach. Given some initial information from such a reference section, we try to derived generalized patterns. These patterns are used to create a local model of the current document. The local model helps to identify errors and to improve the extracted information incrementally during the extraction process. Our approach is implemented with handcrafted transformation rules working on a meta-level being able to correct the information independent of the applied layout style. The experimental results compete very we...

• Type: Journal
• Year: 2010
• Where: KI
• Authors: Peter Klügl, Andreas Hotho, Frank Puppe
• Keywords: Artificial Intelligence, Automatic Extraction Methods, KI 2010, Local Model, Reference Sections