Comparable Corpus Mining

Comparable Corpus Mining is a methodology in computational linguistics that systematically identifies and extracts comparable texts across different languages based on non-parallel but topically or contextually aligned documents, distinguishing it from parallel corpus construction by relying on semantic and situational similarity rather than direct translation equivalence.

103

Publications

4.1K

Citations

235

Authors

116

Institutions

Publications per year

2017–2026

20

Authors

235

  1. SH
    Seung-won Hwang

    Pohang University of Science and Technology, South Korea

    4 publications · 54 citations

    4

    h-index

  2. PZ
    Pierre Zweigenbaum

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

    4 publications · 229 citations

    4

    h-index

  3. HS
    Holger Schwenk

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

    4 publications · 284 citations

    4

    h-index

  4. RG
    Robert Gaizauskas

    University of Sheffield, United Kingdom

    4 publications · 152 citations

    4

    h-index

  5. EM
    Emmanuel Morin

    Nantes Université, France

    3 publications · 133 citations

    3

    h-index

1–5 of 235

Venues

  1. HAL (Le Centre pour la Communication Scientifique Directe)

    Repository · 4 publications · 112 citations

    4

    h-index

  2. International Conference on Computational Linguistics

    Conference · 2 publications · 46 citations

    2

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 39 citations

    2

    h-index

  4. Machine Translation

    2 publications · 96 citations

    2

    h-index

  5. IEICE Transactions on Information and Systems

    2 publications · 23 citations

    2

    h-index

1–5