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        <title>R-grams: Unsupervised Learning of Semantic Units in Natural Language</title>
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        <namePart type="given">Amaru</namePart>
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        <name type="personal">
            <namePart type="given">Vladislav</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T R-grams: Unsupervised Learning of Semantic Units in Natural Language
%A Gyllensten, Amaru Cuba
%A Ekgren, Ariel
%A Sahlgren, Magnus
%Y Dobnik, Simon
%Y Chatzikyriakidis, Stergios
%Y Demberg, Vera
%Y Abu Kwaik, Kathrein
%Y Maraev, Vladislav
%S Proceedings of the 13th International Conference on Computational Semantics - Student Papers
%D 2019
%8 May
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F gyllensten-etal-2019-r
%X 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.
%R 10.18653/v1/W19-0607
%U https://aclanthology.org/W19-0607/
%U https://doi.org/10.18653/v1/W19-0607
%P 52-62

Markdown (Informal)

[R-grams: Unsupervised Learning of Semantic Units in Natural Language](https://aclanthology.org/W19-0607/) (Gyllensten et al., IWCS 2019)

• R-grams: Unsupervised Learning of Semantic Units in Natural Language (Gyllensten et al., IWCS 2019)

ACL

• Amaru Cuba Gyllensten, Ariel Ekgren, and Magnus Sahlgren. 2019. R-grams: Unsupervised Learning of Semantic Units in Natural Language. In Proceedings of the 13th International Conference on Computational Semantics - Student Papers, pages 52–62, Gothenburg, Sweden. Association for Computational Linguistics.