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
M. Granitzer, M. Hristakeva, R. Knight, K. Jack, and R. Kern. Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, page 19:1--19:8. New York, NY, USA, ACM, (2012)
T. Rattenbury, N. Good, and M. Naaman. SIGIR '07: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, page 103--110. New York, NY, USA, ACM Press, (2007)
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)
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)
M. Kayed, and K. Shaalan. IEEE Transactions on Knowledge and Data Engineering, 18 (10):
1411--1428(2006)Member-Chia-Hui Chang and Member-Moheb Ramzy Girgis.
G. Gottlob, C. Koch, R. Baumgartner, M. Herzog, and S. Flesca. Proceedings of the Twenty-third ACM SIGACT-SIGMOD-SIGART Symposium
on Principles of Database Systems, June 14-16, 2004, Paris, France, page 1-12. ACM, (2004)