Extraction of Information From Public Health Emergency Web Documents
Li Wang, Yuanpeng Zhang, Danmin Qian, Min Yao · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2015
Globalization and economic growth have brought more and more uncertain factors that would lead to the occurrence of public health emergencies, which greatly threaten people's lives and properties.The occurrence of a public health emergency is often accompanied by the appearance of a huge amount of related documents on the Internet, and these documents carry a lot of important information.To extract such information, which are usually stored in the form of plain texts (unstructured documents) and cannot be reused directly, it is crucial to automate the extraction process.This work proposed a method for the recognition of named entities with H7N9 public health emergency-related web documents as the research subject, using Hidden Markov Models.The experimental results showed that the proposed method could effectively extract time, location and symptom information.