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Multilingual culture-independent word analogy datasets

  • Matej Ular
  • , Kristiina Vaik
  • , Jessica Lindström
  • , Milda Kurpniece
  • , Marko Robnik-Šikonja
  • University of Ljubljana
  • University of Tartu
  • University of Helsinki

Research output: Chapter in Book/Report/Conference proceedingConference paperResearchpeer-review

9 Citations (Scopus)

Abstract

In text processing, deep neural networks mostly use word embeddings as an input. Embeddings have to ensure that relations between words are reflected through distances in a high-dimensional numeric space. To compare the quality of different text embeddings, typically, we use benchmark datasets. We present a collection of such datasets for the word analogy task in nine languages: Croatian, English, Estonian, Finnish, Latvian, Lithuanian, Russian, Slovenian, and Swedish. We designed the monolingual analogy task to be much more culturally independent and also constructed cross-lingual analogy datasets for the involved languages. We present basic statistics of the created datasets and their initial evaluation using fastText embeddings.

Original languageEnglish
Title of host publicationLREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
EditorsNicoletta Calzolari, Frederic Bechet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Place of PublicationParis
PublisherThe European Language Resources Association
Pages4074-4080
Number of pages7
ISBN (Electronic)9791095546344
ISBN (Print)979-109554634-4, 9791095546344
Publication statusPublished - 2020

Publication series

NameLREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings

Keywords

  • Analogy task
  • Evaluation
  • Less-resourced languages
  • Word embeddings

OECD Field of Science

  • 6.2 Languages and Literature

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