Linking WordNet to 3D Shapes

Angel X Chang, Rishi Mago, Pranav Krishna, Manolis Savva, Christiane Fellbaum


Abstract
We describe a project to link the Princeton WordNet to 3D representations of real objects and scenes. The goal is to establish a dataset that helps us to understand how people categorize everyday common objects via their parts, attributes, and context. This paper describes the annotation and data collection effort so far as well as ideas for future work.
Anthology ID:
2018.gwc-1.44
Volume:
Proceedings of the 9th Global Wordnet Conference
Month:
January
Year:
2018
Address:
Nanyang Technological University (NTU), Singapore
Venue:
GWC
SIG:
Publisher:
Global Wordnet Association
Note:
Pages:
358–363
Language:
URL:
https://aclanthology.org/2018.gwc-1.44
DOI:
Bibkey:
Cite (ACL):
Angel X Chang, Rishi Mago, Pranav Krishna, Manolis Savva, and Christiane Fellbaum. 2018. Linking WordNet to 3D Shapes. In Proceedings of the 9th Global Wordnet Conference, pages 358–363, Nanyang Technological University (NTU), Singapore. Global Wordnet Association.
Cite (Informal):
Linking WordNet to 3D Shapes (Chang et al., GWC 2018)
Copy Citation:
PDF:
https://aclanthology.org/2018.gwc-1.44.pdf
Data
ImageNetSUNCGShapeNet