Abstract
In this paper, we propose a new dynamic template based event detection algorithm (DTED). Candidate template of an event is firstly constructed from a set of texts or their surrogates. Each candidate template contains several terms automatically extracted by the term weighting algorithm proposed in this paper. Then, we classify each text into a candidate event through a new similarity function. Some insignificant candidate templates are deleted. Whether an event template represents a new happened event or not is determined by comparing it with the event templates constructed in previous time window. Some events are merged into existing events and their templates are updated again. To evaluate the proposed DTED algorithm, we construct two datasets for experiment and F-measure is used as performance metric. The experiment result shows that DTED outperforms single-pass algorithm and clustering algorithms implemented in Cluto toolkit; meanwhile, Experimental results on Linguistic Data Consortium (LDC) dataset TDT4 show that DTED gets promising result.
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Wang, D., Chen, Q., Wang, X., Weng, J. (2011). Dynamic Template Based Online Event Detection. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_18
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DOI: https://doi.org/10.1007/978-3-642-24965-5_18
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