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Unsupervised Inference of Object Affordance from Text Corpora
| Content Provider | Semantic Scholar |
|---|---|
| Author | Hartmann, Mareike |
| Copyright Year | 2019 |
| 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. Affordance; Natural Language Processing; Robotics; Intention Recognition; Conditional Varia- |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.diva-portal.org/smash/get/diva2:1351658/FULLTEXT02.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
