Cross-language Topic Detection

Cross-Language Topic Detection is a computational task in cross-language information retrieval that identifies and extracts semantically equivalent topics across documents written in different languages by leveraging cross-lingual semantic representations and alignment techniques.

29

Publications

1.2K

Citations

97

Authors

41

Institutions

Publications per year

2017–2026

8

Authors

97

  1. MM
    Marie‐Francine Moens

    KU Leuven, Belgium

    2 publications · 54 citations

    2

    h-index

  2. IV
    Ivan Vulić

    University of Cambridge, United Kingdom

    2 publications · 54 citations

    2

    h-index

  3. PP
    Peter Prettenhofer

    Bauhaus-Universität Weimar, Germany

    1 publications · 67 citations

    1

    h-index

  4. XC
    Xueqi Cheng

    Chinese Academy of Sciences, China

    1 publications · 18 citations

    1

    h-index

  5. XJ
    Xiaolong Jin

    Institute of Computing Technology, China

    1 publications · 18 citations

    1

    h-index

1–5 of 97

Venues

  1. Communication Methods and Measures

    CMM · 1 publications · 33 citations

    1

    h-index

  2. Lirias (KU Leuven)

    Repository · 1 publications · 35 citations

    1

    h-index

  3. ACM Transactions on Knowledge Discovery from Data

    TKDD · 1 publications · 41 citations

    1

    h-index

  4. 1

    h-index

  5. Companion Proceedings of the Web Conference 2020

    Conference · 1 publications · 12 citations

    1

    h-index

1–5