Abstract
Derivational relations are an important element in defining meanings, as they help to explore word-formation schemes and predict senses of derivates (derived words). In this work, we analyse different methods of representing derivational forms obtained from WordNet – from quantitative vectors to contextual learned embedding methods – and compare ways of classifying the derivational relations occurring between them. Our research focuses on the explainability of the obtained representations and results. The data source for our research is plWordNet, which is the wordnet of the Polish language and includes a rich set of derivation examples.- Anthology ID:
- 2023.gwc-1.39
- Volume:
- Proceedings of the 12th Global Wordnet Conference
- Month:
- January
- Year:
- 2023
- Address:
- University of the Basque Country, Donostia - San Sebastian, Basque Country
- Editors:
- German Rigau, Francis Bond, Alexandre Rademaker
- Venue:
- GWC
- SIG:
- Publisher:
- Global Wordnet Association
- Note:
- Pages:
- 325–330
- Language:
- URL:
- https://aclanthology.org/2023.gwc-1.39
- DOI:
- Cite (ACL):
- Wiktor Walentynowicz and Maciej Piasecki. 2023. Wordnet-oriented recognition of derivational relations. In Proceedings of the 12th Global Wordnet Conference, pages 325–330, University of the Basque Country, Donostia - San Sebastian, Basque Country. Global Wordnet Association.
- Cite (Informal):
- Wordnet-oriented recognition of derivational relations (Walentynowicz & Piasecki, GWC 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.gwc-1.39.pdf