That Makes Sense: Joint Sense Retrofitting from Contextual and Ontological Information.
Journal
Companion of the The Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon , France, April 23-27, 2018
Pages
15-16
Date Issued
2018
Author(s)
Abstract
While recent word embedding models demonstrate their abilities to capture syntactic and semantic information, the demand for sense level embedding is getting higher. In this study, we propose a novel joint sense embedding learning model that retrofits the word representation into sense representation from contextual and ontological information. The experiment shows the effectiveness and robustness of our model that outperforms previous approaches in four public available benchmark datasets.
Type
conference paper