GenSense: A Generalized Sense Retrofitting Model

Yang-Yin Lee, Ting-Yu Yen, Hen-Hsen Huang, Yow-Ting Shiue, Hsin-Hsi Chen


Abstract
With the aid of recently proposed word embedding algorithms, the study of semantic similarity has progressed and advanced rapidly. However, many natural language processing tasks need sense level representation. To address this issue, some researches propose sense embedding learning algorithms. In this paper, we present a generalized model from existing sense retrofitting model. The generalization takes three major components: semantic relations between the senses, the relation strength and the semantic strength. In the experiment, we show that the generalized model can outperform previous approaches in three types of experiment: semantic relatedness, contextual word similarity and semantic difference.
Anthology ID:
C18-1141
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1662–1671
Language:
URL:
https://aclanthology.org/C18-1141
DOI:
Bibkey:
Cite (ACL):
Yang-Yin Lee, Ting-Yu Yen, Hen-Hsen Huang, Yow-Ting Shiue, and Hsin-Hsi Chen. 2018. GenSense: A Generalized Sense Retrofitting Model. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1662–1671, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
GenSense: A Generalized Sense Retrofitting Model (Lee et al., COLING 2018)
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PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/C18-1141.pdf
Code
 y95847frank/GenSense