DESRM: a disease extraction system for real-time monitoring
by Minh-Tien Nguyen; Tri-Thanh Nguyen
International Journal of Computational Vision and Robotics (IJCVR), Vol. 5, No. 3, 2015

Abstract: In this paper, we proposed a method that combines semantic rules and machine learning to extract infectious disease events in Vietnamese electronic news for a real-time monitoring system of spreading status. Our method includes two important steps: detecting disease events from unstructured text and extracting information of the disease event. The detection phrase uses semantic rules and machine learning to detect a disease event; in the later step, named entity recognition (NER), rules, and dictionaries are utilised to capture the events information. The performance of the two steps has F-score of 77.33% (2.36% better than the baseline's) and 91.89% (4.31% better than the baseline's) correspondingly. The promising results from the comparisons showed that our method is suitable for extracting disease events in Vietnamese text.

Online publication date: Fri, 21-Aug-2015

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