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).
seems like it must be viewed with a webkit browser like epiphany or chrome
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To help researchers investigate relation extraction, we’re releasing a human-judged dataset of two relations about public figures on Wikipedia: nearly 10,000 examples of “place of birth”, and over 40,000 examples of “attended or graduated from an institution”. Each of these was judged by at least 5 raters, and can be used to train or evaluate relation extraction systems. We also plan to release more relations of new types in the coming months.
J. Chu-Carroll, und J. Prager. CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, Seite 505--514. New York, NY, USA, ACM, (2007)
L. Lesmo, A. Mazzei, und D. Radicioni. HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, New York, NY, USA, ACM, (Juli 2009)
M. Romanello, M. Berti, A. Babeu, und G. Crane. HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, New York, NY, USA, ACM, (Juli 2009)