@inproceedings{seonwoo-etal-2019-additive,
    title = "Additive Compositionality of Word Vectors",
    author = "Seonwoo, Yeon  and
      Park, Sungjoon  and
      Kim, Dongkwan  and
      Oh, Alice",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/D19-5551/",
    doi = "10.18653/v1/D19-5551",
    pages = "387--396",
    abstract = "Additive compositionality of word embedding models has been studied from empirical and theoretical perspectives. Existing research on justifying additive compositionality of existing word embedding models requires a rather strong assumption of uniform word distribution. In this paper, we relax that assumption and propose more realistic conditions for proving additive compositionality, and we develop a novel word and sub-word embedding model that satisfies additive compositionality under those conditions. We then empirically show our model{'}s improved semantic representation performance on word similarity and noisy sentence similarity."
}