Computer Science – Computation and Language
Scientific paper
2011-10-13
Journal of Biomedical Semantics 2011, 2(Suppl 5):S10
Computer Science
Computation and Language
Scientific paper
Background: Online news reports are increasingly becoming a source for event based early warning systems that detect natural disasters. Harnessing the massive volume of information available from multilingual newswire presents as many challenges as opportunities due to the patterns of reporting complex spatiotemporal events. Results: In this article we study the problem of utilising correlated event reports across languages. We track the evolution of 16 disease outbreaks using 5 temporal aberration detection algorithms on text-mined events classified according to disease and outbreak country. Using ProMED reports as a silver standard, comparative analysis of news data for 13 languages over a 129 day trial period showed improved sensitivity, F1 and timeliness across most models using cross-lingual events. We report a detailed case study analysis for Cholera in Angola 2010 which highlights the challenges faced in correlating news events with the silver standard. Conclusions: The results show that automated health surveillance using multilingual text mining has the potential to turn low value news into high value alerts if informed choices are used to govern the selection of models and data sources. An implementation of the C2 alerting algorithm using multilingual news is available at the BioCaster portal http://born.nii.ac.jp/?page=globalroundup.
No associations
LandOfFree
Towards cross-lingual alerting for bursty epidemic events does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Towards cross-lingual alerting for bursty epidemic events, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Towards cross-lingual alerting for bursty epidemic events will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-521880