Abstract
Anticipatory Event Detection (AED) attempts to monitor user anticipated events, called Anticipatory Events (AE) that have yet to occur. Central to AED is the Event Transition Graph (ETG), which defines the pre and post states of a user specified AE. A classification model can be trained on documents in the pre and post states to learn to detect an AE. However, this simplistic classification model does not make use of discriminatory keywords between the two states. We propose a simple but effective feature selection method to identify important bursty features that highly discriminate between the pre and post states of an AE. Bursty features are first computed using Kleinberg’s Algorithm, then various combination of features in both states are selected. Experimental results show that bursty features can significantly improve the accuracy of AED.
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Aminuddin, R., Suri, R., Chang, K., Zainudin, Z., He, Q., Lim, EP. (2007). Modeling Anticipatory Event Transitions. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_39
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DOI: https://doi.org/10.1007/978-3-540-71701-0_39
Publisher Name: Springer, Berlin, Heidelberg
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