Abstract
The main goal of this research work is to develop a framework with a set of methods for discovery bursty events and their relationships from streams of online news articles. Our aim is to build a fully functional system that indexes news text documents and their corresponding terms occurrences according to the timestamp (temporal indexing) in order to discover bursty terms. The discovery of a bursty event can then be done using the discovered bursty terms which are significantly smaller in size compared to the original feature-set. Furthermore, the discovered bursty events are compared against each other in order to discover any potential relational link between any of two. It is the assumption of this work that the bursty events and their relations in time can provide useful information to firms and individuals whose decision-makings tasks are significantly affected by news events.
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Madani, P. (2015). A Framework for Discovering Bursty Events and Their Relationships from Online News Articles. In: Barbosa, D., Milios, E. (eds) Advances in Artificial Intelligence. Canadian AI 2015. Lecture Notes in Computer Science(), vol 9091. Springer, Cham. https://doi.org/10.1007/978-3-319-18356-5_34
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DOI: https://doi.org/10.1007/978-3-319-18356-5_34
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