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. Dligach, T. Miller, C. Lin, S. Bethard, und G. Savova. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, 2, Seite 746--751. (2017)
M. Hearst. Proceedings of the 14th Conference on Computational Linguistics - Volume 2, Seite 539--545. Stroudsburg, PA, USA, Association for Computational Linguistics, (1992)
R. Baeza-Yates, und A. Tiberi. KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, Seite 76--85. New York, NY, USA, ACM, (2007)
A. Culotta, und J. Sorensen. Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04, Association for Computational Linguistics, (2004)