@inproceedings{persiani-hellstrom-2019-unsupervised,
    title = "Unsupervised Inference of Object Affordance from Text Corpora",
    author = {Persiani, Michele  and
      Hellstr{\"o}m, Thomas},
    editor = "Hartmann, Mareike  and
      Plank, Barbara",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "–" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W19-6112/",
    pages = "115--120",
    abstract = "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."
}