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We call this a spatio-temporal context-aware event representation.
We also address event representation, event hierarchy and expectations, as issues intimately related with time.
In the implementation, an Address Event Representation (AER) is used for transmitting information between neurons.
We describe our empirical findings, which illustrate the usefulness of our proposed event representation.
Section 3 presents our proposed spatio-temporal context-aware news event representation.
Section 6 describes our exploratory data mining analysis using the proposed high-level event representation.
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Specifically, Section 3 extends Quezada et al. [8] by providing a more formal and detailed description of event representations.
We present a general overview of the architecture for generating our event representations in order to use them in our application.
We found that applying standard similarity metrics over the data in the event representations, yielded relationships between certain countries that resemble intense historical interactions and/or geographical proximity.
It is not certain that for other social media platforms we will have enough information, regarding user location and data availability, in order to produce accurate event representations.
The following modules are responsible for the creation of the input dataset from which the event representations are created in the following step.
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event voice
event submission
event proportion
event presence
stage representation
event representative
tour representation
event turnout
business representation
event performance
event observation
test representation
event proportions
event voices
event submissions
event public
event recognition
event definition
event privacy
event food
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