Michele Persiani
Department of Computing Science, Umeå University, Umeå, Sweden
Thomas Hellström
Department of Computing Science, Umeå University, Umeå, Sweden
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Published in: Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland
Linköping Electronic Conference Proceedings 167:12, p. 115--120
NEALT Proceedings Series 42:12, p. 115--120
Published: 2019-10-02
ISBN: 978-91-7929-995-8
ISSN: 1650-3686 (print), 1650-3740 (online)
Affordances denote actions that can be performed in the presence of different objects, or possibility of action in an environment. In robotic systems, affordances and actions may suffer from poor semantic generalization capabilities due to the high amount of required hand-crafted specifications. To alleviate this issue, we propose a method to mine for object-action pairs in free text corpora, successively training and evaluating different prediction models of affordance based on word embeddings.
Affordance
Natural Language Processing
Intention Recognition
Robotics
Conditional Variational Autoencoder
