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Comparison of Cross-Lingual Similar Documents Retrieval Methods

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Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1427))

Abstract

In this paper, we compare different methods for cross-lingual similar document retrieval for distant language pair, namely Russian and English languages. We compare various methods among them: classical Cross-Lingual Explicit Semantic Analysis (CL-ESA), machine translation methods and approaches based on cross-lingual embeddings. We introduce two datasets for evaluation of this task: Russian-English aligned Wikipedia articles and automatically translated Paraplag. Conducted experiments show that an approach with inverted index, with an extra step of mapping top keywords from one language to other with the help of cross-lingual word embeddings, achieves better performance in terms of recall and MAP than other methods on both datasets.

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Notes

  1. 1.

    russian-syntagrus-ud-2.5–191206 and english-ewt-ud-2.5–191206 models.

  2. 2.

    Russian-English parallel corpus: https://translate.yandex.ru/corpus?lang=en/.

  3. 3.

    A library for Multilingual Unsupervised or Supervised word Embeddings https://github.com/facebookresearch/MUSE.

  4. 4.

    version 1.1.1.

  5. 5.

    http://nlp.isa.ru/ru-en-src-retr-dataset/.

  6. 6.

    http://opus.nlpl.eu/WMT-News.php.

  7. 7.

    http://opus.nlpl.eu/News-Commentary.php.

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Acknowledgement

The reported study was funded by RFBR according to the research projects No 18–37-20017 & No 18–29-03187. This research is also partially supported by the Ministry of Science and Higher Education of the Russian Federation according to the agreement between the Lomonosov Moscow State University and the Foundation of project support of the National Technology Initiative No 13/1251/2018 dated 11.12.2018 within the Research Program “Center of Big Data Storage and Analysis” of the National Technology Initiative Competence Center (project “Text mining tools for big data”).

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Authors and Affiliations

  1. Federal Research Center ‘Computer Science and Control’ of Russian Academy of Sciences, 44-2 Vavilov Street, Moscow, 119333, Russia

    Denis Zubarev & Ilya Sochenkov

Authors
  1. Denis Zubarev
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  2. Ilya Sochenkov
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Correspondence to Denis Zubarev .

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Editors and Affiliations

  1. Voronezh State University, Voronezh, Russia

    Alexander Sychev

  2. Voronezh State University, Voronezh, Russia

    Sergey Makhortov

  3. Christian-Albrecht University of Kiel, Kiel, Schleswig-Holstein, Germany

    Bernhard Thalheim

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Zubarev, D., Sochenkov, I. (2021). Comparison of Cross-Lingual Similar Documents Retrieval Methods. In: Sychev, A., Makhortov, S., Thalheim, B. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2020. Communications in Computer and Information Science, vol 1427. Springer, Cham. https://doi.org/10.1007/978-3-030-81200-3_16

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  • DOI: https://doi.org/10.1007/978-3-030-81200-3_16

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