Resource-poor Language Retrieval

Resource-Poor Language Retrieval is a type of cross-language information retrieval approach that specifically addresses the challenge of retrieving information in languages with limited linguistic resources, such as annotated corpora, lexicons, or pre-trained models, by leveraging transfer from resource-rich languages or innovative data-scarce techniques.

28

Publications

2.9K

Citations

109

Authors

42

Institutions

Publications per year

2017–2026

6

Variants

Low-resource Language Retrieval

Authors

109

  1. LM
    Lidia Mangu

    IBM Research - Thomas J. Watson Research Center, United States

    4 publications · 114 citations

    4

    h-index

  2. VS
    Víctor Soto

    Columbia University, United States

    3 publications · 61 citations

    3

    h-index

  3. JH
    Julia Hirschberg

    Columbia University, United States

    3 publications · 61 citations

    3

    h-index

  4. XC
    Xiaodong Cui

    IBM (United States), United States

    2 publications · 90 citations

    2

    h-index

  5. AR
    Andrew Rosenberg

    Columbia University, United States

    2 publications · 24 citations

    2

    h-index

1–5 of 109

Venues

  1. Journal of Artificial Intelligence Research

    JAIR · 1 publications · 20 citations

    1

    h-index

  2. 1

    h-index

  3. Neural Networks

    1 publications · 16 citations

    1

    h-index

  4. Figshare

    Repository · 1 publications · 26 citations

    1

    h-index

  5. Polibits

    1 publications · 30 citations

    1

    h-index

1–5