@inproceedings{jang-etal-2018-interpretable,
    title = "Interpretable Word Embedding Contextualization",
    author = "Jang, Kyoung-Rok  and
      Myaeng, Sung-Hyon  and
      Kim, Sang-Bum",
    editor = "Linzen, Tal  and
      Chrupa{\l}a, Grzegorz  and
      Alishahi, Afra",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W18-5442/",
    doi = "10.18653/v1/W18-5442",
    pages = "341--343",
    abstract = "In this paper, we propose a method of calibrating a word embedding, so that the semantic it conveys becomes more relevant to the context. Our method is novel because the output shows clearly which senses that were originally presented in a target word embedding become stronger or weaker. This is possible by utilizing the technique of using sparse coding to recover senses that comprises a word embedding."
}