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).
D. Dimitrov, P. Singer, D. Helic, and M. Strohmaier. Proceedings of the 26th ACM Conference on Hypertext &\#38; Social Media, page 59--68. New York, NY, USA, ACM, (2015)
E. Chi, P. Pirolli, K. Chen, and J. Pitkow. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, page 490--497. New York, NY, USA, ACM, (2001)
T. Joachims. Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, page 133--142. New York, NY, USA, ACM, (2002)
P. Kluegl, M. Atzmueller, and F. Puppe. Proc. LWA 2009, Knowledge Discovery and Machine Learning Track, Darmstadt, Germany, University of Darmstadt, (2009)
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), accepted, (2008)
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)