Transformer-based Tagging

Transformer-Based Tagging is a part-of-speech tagging method that employs transformer architectures to model contextual dependencies in text through self-attention mechanisms, distinguishing itself from rule-based, statistical, or recurrent neural approaches by leveraging parallelized, context-aware representations for improved tag prediction accuracy.

39

Publications

1.6K

Citations

174

Authors

62

Institutions

Publications per year

2017–2026

39

Authors

174

  1. DS
    David Stap

    University of Amsterdam, Netherlands

    2 publications · 286 citations

    2

    h-index

  2. IP
    Ishan Purohit

    International Institute for Applied Systems Analysis, Austria

    2 publications · 286 citations

    2

    h-index

  3. KO
    Kostiantyn Omelianchuk

    Ukrainian Catholic University, Ukraine

    2 publications · 54 citations

    2

    h-index

  4. YW
    Yizhong Wang

    Chinese Academy of Sciences, China

    2 publications · 286 citations

    2

    h-index

  5. KD
    Krima Doshi

    University of Amsterdam, Netherlands

    2 publications · 286 citations

    2

    h-index

1–5 of 174

Venues

  1. arXiv (Cornell University)

    Repository · 11 publications · 515 citations

    10

    h-index

  2. 2

    h-index

  3. 1

    h-index

  4. JAMIA Open

    1 publications · 38 citations

    1

    h-index

  5. Data and Information Management

    1 publications · 15 citations

    1

    h-index

1–5