Abstract
Words are defined based on their meanings in various ways in different resources. Aligning word senses across monolingual lexicographic resources increases domain coverage and enables integration and incorporation of data. In this paper, we explore the application of classification methods using manually-extracted features along with representation learning techniques in the task of word sense alignment and semantic relationship detection. We demonstrate that the performance of classification methods dramatically varies based on the type of semantic relationships due to the nature of the task but outperforms the previous experiments.- Anthology ID:
- 2021.gwc-1.9
- Volume:
- Proceedings of the 11th Global Wordnet Conference
- Month:
- January
- Year:
- 2021
- Address:
- University of South Africa (UNISA)
- Editors:
- Piek Vossen, Christiane Fellbaum
- Venue:
- GWC
- SIG:
- SIGLEX
- Publisher:
- Global Wordnet Association
- Note:
- Pages:
- 73–80
- Language:
- URL:
- https://aclanthology.org/2021.gwc-1.9
- DOI:
- Cite (ACL):
- Sina Ahmadi and John P. McCrae. 2021. Monolingual Word Sense Alignment as a Classification Problem. In Proceedings of the 11th Global Wordnet Conference, pages 73–80, University of South Africa (UNISA). Global Wordnet Association.
- Cite (Informal):
- Monolingual Word Sense Alignment as a Classification Problem (Ahmadi & McCrae, GWC 2021)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2021.gwc-1.9.pdf