Journal Article

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

Nigel Collier, Son Doan, Ai Kawazoe, Reiko Matsuda Goodwin, Mike Conway, Yoshio Tateno, Quoc-Hung Ngo, Dinh Dien, Asanee Kawtrakul, Koichi Takeuchi, Mika Shigematsu and Kiyosu Taniguchi

in Bioinformatics

Volume 24, issue 24, pages 2940-2941
Published in print December 2008 | ISSN: 1367-4803
Published online October 2008 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn534

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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 1700 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

Journal Article.  1175 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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