BioCaster: detecting public health rumors with a Web-based text mining system Export

@article{citeulike:3420731, abstract = {Summary: BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1,700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information. The background knowledge for bridging the gap between layman's terms and formal coding systems is contained in the freely available BioCaster ontology which includes information in eight languages focused on the epidemiological role of pathogens as well as geographical locations with their latitudes/longitudes. The system consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and event recognition. Higher order event analysis is used to detect more precisely specified warning signals that can then be notified to registered users via email alerts. Evaluation of the system for topic recognition and entity identification is conducted on a gold standard corpus of annotated news articles. Availability: The BioCaster map and ontology are freely available via a web portal at http://www.biocaster.org Contact: collier@nii.ac.jp Supplementary information: XXXX 10.1093/bioinformatics/btn534}, author = {Collier, Nigel and Doan, Son and Kawazoe, Ai and Goodwin, Reiko M. and Conway, Mike and Tateno, Yoshio and Ngo, Quoc-Hung and Dien, Dinh and Kawtrakul, Asanee and Takeuchi, Koichi and Shigematsu, Mika and Taniguchi, Kiyosu}, citeulike-article-id = {3420731}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btn534}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/24/24/2940?rss=1}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/18922806}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=18922806}, doi = {10.1093/bioinformatics/btn534}, journal = {Bioinformatics}, keywords = {software, text-mining}, month = {October}, pages = {btn534+}, posted-at = {2009-03-12 02:46:47}, priority = {2}, title = {BioCaster: detecting public health rumors with a Web-based text mining system}, url = {http://dx.doi.org/10.1093/bioinformatics/btn534}, year = {2008} }

TY - JOUR ID - citeulike:3420731 L3 - citeulike-article-id:3420731 N2 - Summary: BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1,700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information. The background knowledge for bridging the gap between layman's terms and formal coding systems is contained in the freely available BioCaster ontology which includes information in eight languages focused on the epidemiological role of pathogens as well as geographical locations with their latitudes/longitudes. The system consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and event recognition. Higher order event analysis is used to detect more precisely specified warning signals that can then be notified to registered users via email alerts. Evaluation of the system for topic recognition and entity identification is conducted on a gold standard corpus of annotated news articles. Availability: The BioCaster map and ontology are freely available via a web portal at http://www.biocaster.org Contact: collier@nii.ac.jp Supplementary information: XXXX 10.1093/bioinformatics/btn534 JF - Bioinformatics TI - BioCaster: detecting public health rumors with a Web-based text mining system SP - btn534 KW - software KW - text-mining AU - Collier, Nigel AU - Doan, Son AU - Kawazoe, Ai AU - Goodwin, Reiko AU - Conway, Mike AU - Tateno, Yoshio AU - Ngo, Quoc-Hung AU - Dien, Dinh AU - Kawtrakul, Asanee AU - Takeuchi, Koichi AU - Shigematsu, Mika AU - Taniguchi, Kiyosu PY - 2008/10/15/ UR - http://dx.doi.org/10.1093/bioinformatics/btn534 ER -