Conference article

Unsupervised Inference of Object Affordance from Text Corpora

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

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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


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