Neural Sequence Tagging

Neural Sequence Tagging is a neural network–based method for assigning part-of-speech tags to words in a sentence by modeling the sequential dependencies among tokens using architectures such as recurrent neural networks, convolutional neural networks, or transformers.

28

Publications

1.6K

Citations

90

Authors

39

Institutions

Publications per year

2017–2026

25

Authors

90

  1. SL
    Shuailong Liang

    Xiangya Hospital Central South University, China

    2 publications · 203 citations

    2

    h-index

  2. JY
    Jie Yang

    Harvard University, United States

    2 publications · 203 citations

    2

    h-index

  3. YZ
    Yue Zhang

    Beihang University, China

    2 publications · 210 citations

    2

    h-index

  4. SH
    Stephen H. Bach

    Stanford University, United States

    1 publications · 83 citations

    1

    h-index

  5. OK
    Olga Kozlova

    Centre National de la Recherche Scientifique, France

    1 publications · 10 citations

    1

    h-index

1–5 of 90

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 378 citations

    5

    h-index

  2. Neurocomputing

    1 publications · 13 citations

    1

    h-index

  3. IEEE Access

    1 publications · 26 citations

    1

    h-index

  4. International Conference on Computational Linguistics

    Conference · 1 publications · 115 citations

    1

    h-index

  5. BMC Medical Informatics and Decision Making

    1 publications · 11 citations

    1

    h-index

1–5