@inproceedings{alipoormolabashi-schulte-im-walde-2020-variants,
    title = "Variants of Vector Space Reductions for Predicting the Compositionality of {E}nglish Noun Compounds",
    author = "Alipoormolabashi, Pegah  and
      Schulte im Walde, Sabine",
    editor = "Cunha, Rossana  and
      Shaikh, Samira  and
      Varis, Erika  and
      Georgi, Ryan  and
      Tsai, Alicia  and
      Anastasopoulos, Antonios  and
      Chandu, Khyathi Raghavi",
    booktitle = "Proceedings of the Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.winlp-1.13/",
    doi = "10.18653/v1/2020.winlp-1.13",
    pages = "51--54",
    abstract = "Predicting the degree of compositionality of noun compounds is a crucial ingredient for lexicography and NLP applications, to know whether the compound should be treated as a whole, or through its constituents. Computational approaches for an automatic prediction typically represent compounds and their constituents within a vector space to have a numeric relatedness measure for the words. This paper provides a systematic evaluation of using different vector-space reduction variants for the prediction. We demonstrate that Word2vec and nouns-only dimensionality reductions are the most successful and stable vector space reduction variants for our task."
}