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dc.contributor.authorDuboué, Pablo Ariel
dc.contributor.authorDomínguez, Martín Ariel
dc.descriptionSlides de ponencia presentada en Advances in Artificial Intelligence - IBERAMIA 2016. San José, Costa Rica 23-25, November
dc.description.abstractA sub-task of Natural Language Generation (NLG) is the generation of referring expressions (REG). REG algorithms aim to select attributes that unambiguously identify an entity with respect to a set of distractors. Previous work has defined a methodology to evaluate REG algorithms using real life examples with naturally occurring alterations in the properties of referring entities. It has been found that REG algorithms have key parameters tuned to exhibit a large degree of robustness. Using this insight, we present here experiments for learning the order of semantic properties used by a high performing REG algorithm. Presenting experiments on two types of entities (people and organizations) and using different versions of DBpedia (a freely available knowledge base containing information extracted from Wikipedia pages) we found that robustness of the tuned algorithm and its parameters do coincide but more work is needed to learn these parameters from data in a generalizable fashion.en
dc.format.mediumImpreso; Electrónico y/o Digital
dc.relationDel artículo publicado:
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectSemantc orderen
dc.subjectReferring expressionen
dc.titleUsing robustness to learn to order semantic properties in referring expression generationen
dc.description.filFil: Duboué, Pablo Ariel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación;
dc.description.filFil: Domínguez, Martín Ariel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación;
dc.description.fieldCiencias de la Computación
dc.conference.eventIBERAMIA 2016
dc.conference.eventcitySan José
dc.conference.eventcountryCosta Rica
dc.conference.institutionSociedad Iberoamericana de Inteligencia Artificial (IBERAMIA)
dc.conference.journalAdvances in Artificial Intelligence - IBERAMIA 2016
dc.conference.workSlides de la ponencia

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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International