A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
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
A probabilistic model for retrospective news event detection
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Analyzing feature trajectories for event detection
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Topic modeling for OLAP on multidimensional text databases: topic cube and its applications
Statistical Analysis and Data Mining - Best of SDM'09
Boilerplate detection using shallow text features
Proceedings of the third ACM international conference on Web search and data mining
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Making use of social media data in public health
Proceedings of the 21st international conference companion on World Wide Web
Predicting the future impact of news events
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Extracting event-related information from article updates in wikipedia
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Automatic classification of documents in cold-start scenarios
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
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Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. However, state-of-the-art systems for Epidemic Intelligence have not kept the pace with the growing need for more robust public health event detection. In this paper, we propose a game-changing approach where public health events are detected in an unsupervised manner. We address the problems associated with adapting an unsupervised learner to the medical domain and in doing so, propose an approach which combines aspects from different feature-based event detection methods. We evaluate our approach with a real world dataset with respect to the quality of article clusters. Our results show that we are able to achieve a precision of 66% and a recall of 81% when evaluated using manually annotated, real-world data. This shows promising results for the use of such techniques in this new problem setting.