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. Dligach, T. Miller, C. Lin, S. Bethard, and G. Savova. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, 2, page 746--751. (2017)
M. Hearst. Proceedings of the 14th Conference on Computational Linguistics - Volume 2, page 539--545. Stroudsburg, PA, USA, Association for Computational Linguistics, (1992)
P. Kluegl, M. Atzmueller, and F. Puppe. Proc. LWA 2009, Knowledge Discovery and Machine Learning Track, Darmstadt, Germany, University of Darmstadt, (2009)