@inproceedings{gyllensten-etal-2019-r,
    title = "{R}-grams: Unsupervised Learning of Semantic Units in Natural Language",
    author = "Gyllensten, Amaru Cuba  and
      Ekgren, Ariel  and
      Sahlgren, Magnus",
    editor = "Dobnik, Simon  and
      Chatzikyriakidis, Stergios  and
      Demberg, Vera  and
      Abu Kwaik, Kathrein  and
      Maraev, Vladislav",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Student Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-0607/",
    doi = "10.18653/v1/W19-0607",
    pages = "52--62",
    abstract = "This paper investigates data-driven segmentation using Re-Pair or Byte Pair Encoding-techniques. In contrast to previous work which has primarily been focused on subword units for machine translation, we are interested in the general properties of such segments above the word level. We call these segments r-grams, and discuss their properties and the effect they have on the token frequency distribution. The proposed approach is evaluated by demonstrating its viability in embedding techniques, both in monolingual and multilingual test settings. We also provide a number of qualitative examples of the proposed methodology, demonstrating its viability as a language-invariant segmentation procedure."
}