Identifying Negative Exemplars in Grounded Language Data Sets

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dc.contributor.authorPillai, Nisha
dc.contributor.authorMatuszek, Cynthia
dc.date.accessioned2018-09-06T17:41:23Z
dc.date.available2018-09-06T17:41:23Z
dc.date.issued2017
dc.descriptionWorkshop on Spatial-Semantic Representations in Robotics, 2017
dc.description.abstractThere has been substantial work in recent years on grounded language acquisition, in which paired language and sensor data are used to create a model of how linguistic constructs apply to the perceivable world. While powerful, this approach is hindered by the difficulty of obtaining unprompted negative examples of natural language annotations. In this paper, we describe an initial pilot of a system that uses natural language similarity metrics to automatically select negative examples from a paired corpus of perceptual and linguistic data.en_US
dc.description.urihttp://iral.cs.umbc.edu/Pubs/PillaiMatuszekRSS2017_semantics-workshop.pdf
dc.format.extent8 PAGESen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/M2G73772J
dc.identifier.citationNisha Pillai, Cynthia Matuszek, Identifying Negative Exemplars in Grounded Language Data Sets, Robotics: Science and Systems (R:SS) Workshop on Spatial-Semantic Representations in Robotics, 2017en_US
dc.identifier.urihttp://hdl.handle.net/11603/11250
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
dc.subjectgrounded language acquisitionen_US
dc.subjectjoint learning problemen_US
dc.subjectterm frequency-inverse document frequencyen_US
dc.subjectInteractive Robotics and Language Laben_US
dc.titleIdentifying Negative Exemplars in Grounded Language Data Setsen_US
dc.typeTexten_US

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