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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 Using natural language processing to extract health-related causality from Twitter messages ... Doan, Son; Yang, Elly W; Tilak, Sameer. - : arXiv, 2019 BASE Show details 2 Extracting health-related causality from twitter messages using natural language processing Doan, Son; Yang, Elly W.; Tilak, Sameer S.. - : BioMed Central, 2019 BASE Show details 3 A Preliminary Study of Clinical Concept Detection Using Syntactic Relations Torii, Manabu; Yang, Elly W.; Doan, Son. - : American Medical Informatics Association, 2018 BASE Show details 4 Applying Semantic-based Probabilistic Context-Free Grammar to Medical Language Processing – A Preliminary Study on Parsing Medication Sentences Xu, Hua; AbdelRahman, Samir; Lu, Yanxin. - 2011 BASE Show details 5 Recognizing Medication related Entities in Hospital Discharge Summaries using Support Vector Machine Doan, Son; Xu, Hua. - 2010 BASE Show details 6 BioCaster: detecting public health rumors with a Web-based text mining system Collier, Nigel; Doan, Son; Kawazoe, Ai. - : Oxford University Press, 2008 BASE Show details 7 BioCaster: detecting public health rumors with a Web-based text mining system Collier, Nigel; Doan, Son; Kawazoe, Ai; Goodwin, Reiko Matsuda; Conway, Mike; Tateno, Yoshio; Ngo, Quoc-Hung; Dien, Dinh; Kawtrakul, Asanee; Takeuchi, Koichi; Shigematsu, Mika; Taniguchi, Kiyosu. - : Oxford University Press, 2008 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 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. Keyword: Applications Note URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2639299 https://doi.org/10.1093/bioinformatics/btn534 http://www.ncbi.nlm.nih.gov/pubmed/18922806 BASE Hide details

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