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).
M. Schwab, R. Jäschke, and F. Fischer. Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, page 110--115. Association for Computational Linguistics, (2023)
M. Schwab, R. Jäschke, and F. Fischer. Proceedings of the 5th International Conference on Natural Language and Speech Processing, page 282--287. Association for Computational Linguistics, (2022)
F. Arnold, and R. Jäschke. Proceedings of the Workshop Understanding LIterature references in academic full TExt at JCDL 2022, volume 3220 of ULITE-ws '22, page 7--15. CEUR Workshop Proceedings, (2022)