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Unsupervised Inference of Object Affordance from Text Corpora
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Affordance, Natural Language Processing, Robotics, Intention Recognition, Conditional Variational Autoencoder
National Category
Robotics
Identifiers
URN: urn:nbn:se:umu:diva-163356OAI: oai:DiVA.org:umu-163356DiVA, id: diva2:1351658
Conference
22nd Nordic Conference on Computational Linguistics (NoDaLiDa’19), September 30 – October 2, 2019, Turku, Finland
Available from: 2019-09-16 Created: 2019-09-16 Last updated: 2019-09-20

Open Access in DiVA

fulltext(293 kB)23 downloads
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Persiani, MicheleHellström, Thomas
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf