@InProceedings{lee-EtAl:2018:C18-11,
  author    = {Lee, Yang-Yin  and  Yen, Ting-Yu  and  Huang, Hen-Hsen  and  Shiue, Yow-Ting  and  Chen, Hsin-Hsi},
  title     = {GenSense: A Generalized Sense Retrofitting Model},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {1662--1671},
  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.},
  url       = {http://www.aclweb.org/anthology/C18-1141}
}