@inproceedings{el-kass_visual_2015,

title = {A visual and results-driven rules composition approach for better information extraction},

author = {W. El-Kass and S. Gagnon and M. Iglewski},

editor = {Zaremba M. Sasiadek J. Dolgui A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953876235&doi=10.1016%2fj.ifacol.2015.06.067&partnerID=40&md5=3cd38b1d6b9efc819cd882d181cdda92},

doi = {10.1016/j.ifacol.2015.06.067},

isbn = {24058963 (ISSN)},

year  = {2015},

date = {2015-01-01},

booktitle = {IFAC-PapersOnLine},

volume = {28},

pages = {112–117},

abstract = {We present a highly visual process for creating and combining elementary information extraction rules, based on their results, in order to find the rules combination that produces the most accurate information extraction results. A rule's accuracy is determined by its F-Score which is the harmonic mean of the precision and the recall of that rule. Rules are combined using logical OR and AND operators. Running a few hundreds rules combinations over a corpus, in order to determine their accuracies, can take days. Using our approach, millions of rules combinations can be tested and their accuracies (F-Score) can be calculated in few seconds. A prototype was created to demonstrate the effectiveness of our approach. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},

note = {Issue: 3 

Journal Abbreviation: IFAC-PapersOnLine},

keywords = {Automation, F-score, Flow visualization, Harmonic mean, Information analysis, Information extraction, Information extraction rules, Information retrieval, Rule based, Rule composition, Rules composition, Visual process, Visualization},

pubstate = {published},

tppubtype = {inproceedings}

}