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
J. Lee, F. Dernoncourt, und P. Szolovits. SemEval 2017, (2017)cite arxiv:1704.01523Comment: Accepted at SemEval 2017. The first two authors contributed equally to this work.
N. Peng, H. Poon, C. Quirk, K. Toutanova, und W. Yih. ACL, (2017)cite arxiv:1708.03743Comment: Conditional accepted by TACL in December 2016; published in April 2017; presented at ACL in August 2017.
J. Rotsztejn, N. Hollenstein, und C. Zhang. (2018)cite arxiv:1804.02042Comment: Accepted to SemEval 2018 (12th International Workshop on Semantic Evaluation).
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)