Modeling and Using Context: 8th International and by Richard Dapoigny, Patrick Barlatier (auth.), Patrick

By Richard Dapoigny, Patrick Barlatier (auth.), Patrick Brézillon, Patrick Blackburn, Richard Dapoigny (eds.)

This ebook constitutes the court cases of the eighth foreign and Interdisciplinary convention on Modeling and utilizing Context, CONTEXT 2013, held in Annecy, France, in October/November 2013. The 23 complete papers and nine brief papers awarded have been conscientiously reviewed and chosen from quite a few submissions. additionally the publication includes keynote speeches and nine poster papers. They conceal state-of-the-art effects from the big variety of disciplines interested in context, together with: Cognitive Sciences (Linguistics, Psychology, machine technological know-how, Neuroscience), and laptop technological know-how (artificial intelligence, logics, ubiquitous and pervasive computing, context-awareness systems), and the Social Sciences and Organizational Sciences, in addition to the arts and all software components, together with medication and Law.

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Additional resources for Modeling and Using Context: 8th International and Interdisciplinary Conference, CONTEXT 2013, Annecy, France, October 28 -31, 2013, Proceedings

Sample text

Unlike CrispAdjective, a GradableAdjective does not require a value parameter when initialized, because its value comes from the partial interpretation it is applied to (Q5); it adds intervals values to the belief state’s description rather than atomic values: Gradable adjectives yield an effect for each same-named attribute (ambiguity) for each value (vagueness2 (borderline cases)). For example, given the action 5 Rather, they generate effect functions that operate on states. To simplify the exposition, we pretend our actions are like those of typical planners and generate states.

Scene used during the collection of the TUNA corpus. The human RE was blue frontal chair, and the system the blue chair in the bottom. Both human judges prefer the system generated RE. In Figure 7 the human RE was “blue frontal chair”, and the system RE was “the blue chair in the bottom”; both judges selected the system RE. This case can be explained by the fact that, in this domain, the property “bottom” helps more during the identification than the property “frontal” because it concentrates the attention of the interpreter in the lower part of the scene.

The Association for Computational Linguistics, USA (2008) 14. : USP-EACH frequency-based greedy attribute selection for referring expressions generation.

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