Parallel Corpus Mining

Parallel Corpus Mining is a multilingual natural language processing task that involves the identification and extraction of sentence-aligned document pairs from comparable or noisy monolingual corpora across different languages, distinguished by its focus on discovering high-quality parallel data without reliance on pre-existing translations.

187

Publications

12.9K

Citations

481

Authors

153

Institutions

Publications per year

2017–2026

51

Authors

481

  1. HS
    Holger Schwenk

    Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur, France

    8 publications · 1.4K citations

    8

    h-index

  2. JT
    Jörg Tiedemann

    Uppsala University, Sweden

    5 publications · 276 citations

    5

    h-index

  3. PK
    Philipp Koehn

    University of Edinburgh, United Kingdom

    5 publications · 344 citations

    5

    h-index

  4. MA
    Mikel Artetxe

    University of the Basque Country, Spain

    4 publications · 1K citations

    4

    h-index

  5. MV
    Martijn van der Klis

    Utrecht University, Netherlands

    4 publications · 117 citations

    4

    h-index

1–5 of 481

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 1.2K citations

    10

    h-index

  2. Computational Linguistics

    5 publications · 1.2K citations

    5

    h-index

  3. Meeting of the Association for Computational Linguistics

    Conference · 3 publications · 71 citations

    3

    h-index

  4. Natural Language Engineering

    3 publications · 66 citations

    3

    h-index

  5. Information Processing & Management

    2 publications · 35 citations

    2

    h-index

1–5