Automatic Identification of Basic-Level Categories

Chad Mills, Francis Bond, Gina-Anne Levow


Abstract
Basic-level categories have been shown to be both psychologically significant and useful in a wide range of practical applications. We build a rule-based system to identify basic-level categories in WordNet, achieving 77% accuracy on a test set derived from prior psychological experiments. With additional annotations we found our system also has low precision, in part due to the existence of many categories that do not fit into the three classes (superordinate, basic-level, and subordinate) relied on in basic-level category research.
Anthology ID:
2018.gwc-1.35
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:
298–305
Language:
URL:
https://aclanthology.org/2018.gwc-1.35
DOI:
Bibkey:
Cite (ACL):
Chad Mills, Francis Bond, and Gina-Anne Levow. 2018. Automatic Identification of Basic-Level Categories. In Proceedings of the 9th Global Wordnet Conference, pages 298–305, Nanyang Technological University (NTU), Singapore. Global Wordnet Association.
Cite (Informal):
Automatic Identification of Basic-Level Categories (Mills et al., GWC 2018)
Copy Citation:
PDF:
https://aclanthology.org/2018.gwc-1.35.pdf