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).
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
P. Kluegl, M. Toepfer, P. Beck, G. Fette, and F. Puppe. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations, page 29--33. Dublin, Ireland, Dublin City University and Association for Computational Linguistics, (August 2014)
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. 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)