Extraction of disease events for a real-time monitoring system

Abstract

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.