On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Information extraction for enhanced access to disease outbreak reports
Journal of Biomedical Informatics - Special issue: Sublanguage
Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Real-time event extraction for infectious disease outbreaks
HLT '02 Proceedings of the second international conference on Human Language Technology Research
An ontology-driven system for detecting global health events
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
VnLoc: A Real -- Time News Event Extraction Framework for Vietnamese
KSE '12 Proceedings of the 2012 Fourth International Conference on Knowledge and Systems Engineering
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In this paper, we propose a method that uses both semantic rules and machine learning to extract infectious disease events in Vietnamese electronic news, which can be used in a real-time system of monitoring the spread of diseases. Our method contains two important steps: detecting disease events from unstructured data and extracting information of the disease events. The event detection uses semantic rules and machine learning to detect a disease event; in the later step, Name Entity Recognition (NER), rules, and dictionaries are used to capture the event's information. The performance of detection step is ≈77,33% (F-score) and the precision of extraction step is ≈91,89%. These results are better that those of the experiments in which rules were not used. This indicates that our method is suitable for extracting disease events in Vietnamese text.