Pillai, NishaMatuszek, Cynthia2018-09-062018-09-062017Nisha Pillai, Cynthia Matuszek, Identifying Negative Exemplars in Grounded Language Data Sets, Robotics: Science and Systems (R:SS) Workshop on Spatial-Semantic Representations in Robotics, 2017http://hdl.handle.net/11603/11250Workshop on Spatial-Semantic Representations in Robotics, 2017There 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.8 PAGESen-USThis 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.grounded language acquisitionjoint learning problemterm frequency-inverse document frequencyInteractive Robotics and Language LabText