2014
DOI: 10.1007/978-3-319-12337-0_18
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Ontology–Based Visualization of Characters’ Intentions

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Cited by 6 publications
(6 citation statements)
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“…Regarding the processing time during the prediction stage, the exploitation of the properties (6) and (15) enables a quick mapping through GA. Moreover, our MAP algorithm seems consistent, in the sense that it presents a small standard deviation on the CPU time, as can be seen in Table XVI, which summarizes the mean and standard deviation values of the CPU time demanded to solve the MAP problem for the chosen stories, running on quadcore processor, by using our special formulation of GA, henceforward called SGA, and two baseline algorithms: the usual GA (without exploiting the properties given by (6) and (15)) and random-restart hill climbing (RRHC). In this experiment the number of GA individuals and the number of restarting loops (in the case of RRHC algorithm) were chosen aiming at overcoming local minima, in such a way that the choice of the MAP algorithm has no impact on the performance indices.…”
Section: Methodsmentioning
confidence: 59%
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“…Regarding the processing time during the prediction stage, the exploitation of the properties (6) and (15) enables a quick mapping through GA. Moreover, our MAP algorithm seems consistent, in the sense that it presents a small standard deviation on the CPU time, as can be seen in Table XVI, which summarizes the mean and standard deviation values of the CPU time demanded to solve the MAP problem for the chosen stories, running on quadcore processor, by using our special formulation of GA, henceforward called SGA, and two baseline algorithms: the usual GA (without exploiting the properties given by (6) and (15)) and random-restart hill climbing (RRHC). In this experiment the number of GA individuals and the number of restarting loops (in the case of RRHC algorithm) were chosen aiming at overcoming local minima, in such a way that the choice of the MAP algorithm has no impact on the performance indices.…”
Section: Methodsmentioning
confidence: 59%
“…where the action or state is expressed by a verb in the sentence, and noun-centered. Some works, such as [6], aim at determining the character intentions, to provide the motivations for the actions performed. In the first instance this information can be useful in supporting the narrative interpretation, but in a second instance it can also improve the accuracy in predicting the correct action [7].…”
Section: State Of the Artmentioning
confidence: 99%
“…In order to provide a visualization of the overall annotation we resort to a Processing Sketch [18], that, from the owl, produces a score of intentional and purposive actions, Figure 2. It is interactive, thus the whole image is zoomable and some details (such as the descriptions of the units or plans) are available "on mouse over" (see Figure 3).…”
Section: Results Of Annotationmentioning
confidence: 99%
“…These character-centered description issues, not encompassed by the annotation languages above, are accounted for by the ontology-based story annotation in [14] and [15]. The ontology of drama called Drammar grounds the representation of characters upon the notion of agents' intention (realized through the notion of plan).…”
Section: The Annotation Of Storiesmentioning
confidence: 99%