Automatic text filtering using limited supervision learning for epidemic intelligence

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Identifying Relevant Temporal Expressions for Real-World Events

Event detection is an interesting task for many applications, for instance: surveillance, scientific discovery, and Topic De-tection and Tracking. Numerous works have focused on de-tecting events from unstructured text and determining what features constitutes an event, e.g., key terms or named enti-ties. Although most works are able to find interesting time associated to an event, there is a lack in research on de-termining the relevance of time for an event. In this paper, we propose a method for automatically extracting real-world events from unstructured text documents. In addition, we propose a machine learning approach to identifying relevant time (i.e., temporal expressions) for the extracted events using three classes of features: sentence-based, document-based and corpus-specific features. Through experiments using real-world data and 3,500 manually judged relevance pairs, we show that our proposed approach is able to identify the relevant time of events with good accuracy

Supporting temporal analytics for health-related events in microblogs

Microblogging services, such as Twitter, are gaining inter-ests as a means of sharing information in social networks. Numerous works have shown the potential of using Twitter messages (or tweets) in order to infer the existence and mag-nitude of real-world events. In the medical domain, there has been a surge in detecting public health related tweets for early warning so that a rapid response from health au-thorities can take place. In this paper, we present a tem-poral analytics tool for supporting a comparative, tempo-ral analysis of disease outbreaks between Twitter and of-ficial sources, such as, World Health Organization (WHO) and ProMED-mail. We automatically extract and aggre-gate outbreak events from official outbreak reports in order to produce time series data used for the analysis. Our tool can support a correlation analysis and an understanding of the temporal developments of outbreak mentions in Twitter, based on comparisons with official sources