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).
Neil Ireson, Fabio Ciravegna, Marie Elaine Califf, Dayne Freitag, Nicholas Kushmerick, Alberto Lavelli: Evaluating Machine Learning for Information Extraction, 22nd International Conference on Machine Learning (ICML 2005), Bonn, Germany, 7-11 August, 2005
D. Knoell, M. Atzmueller, C. Rieder, and K. Scherer. Proc. GWEM 2017, co-located with 9th Conference Professional Knowledge Management (WM 2017), Karlsruhe, Germany, KIT, (2017)
P. Kluegl, M. Atzmueller, and F. Puppe. Proc. LWA 2009, Knowledge Discovery and Machine Learning Track, Darmstadt, Germany, University of Darmstadt, (2009)
M. Atzmueller, and S. Beer. Proc. 55th IWK, International Workshop on Design, Evaluation and Refinement of Intelligent Systems (DERIS), University of Ilmenau, (2010)
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)
P. Kluegl, M. Atzmueller, and F. Puppe. Proc. 4th International Workshop on Knowledge Engineering and Software Engineering (KESE 2008), 31th German Conference on Artificial Intelligence (KI-2008), (2008)
Y. Jin, Y. Matsuo, and M. Ishizuka. Proceedings of the European Semantic Web Conference, ESWC2007, volume 4519 of Lecture Notes in Computer Science, Springer-Verlag, (July 2007)