This is the home page of the ParsCit project, which performs reference string parsing, sometimes also called citation parsing or citation extraction. It is architected as a supervised machine learning procedure that uses Conditional Random Fields as its learning mechanism. You can download the code below, parse strings online, or send batch jobs to our web service (coming soon!). The code contains both the training data, feature generator and shell scripts to connect the system to a web service (used here too).
P. Kluegl, M. Toepfer, F. Lemmerich, A. Hotho, and F. Puppe. Proceedings of 1st International Conference on Pattern Recognition Applications and Methods (ICPRAM), page 240-248. Vilamoura, Algarve, Portugal, SciTePress, (6-8 02 2012)
J. Lafferty, A. McCallum, and F. Pereira. Proceedings of the Eighteenth International Conference on Machine Learning, page 282--289. San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., (2001)
J. Tang, M. Hong, J. Li, and B. Liang. International Semantic Web Conference, volume 4273 of Lecture Notes in Computer Science, page 640-653. Springer, (2006)