Identifying Relevant Temporal Expressions for Real-World Events
Nattiya Kanhabua, Sara Romano, Avaré Stewart · 2012
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.