Neural Morphological Analysis

Neural Morphological Analysis is a computational method that employs artificial neural networks to model and predict the internal structure of words by learning distributed representations of morphological patterns and affixation rules from raw text data.

25

Publications

1.1K

Citations

86

Authors

35

Institutions

Publications per year

2017–2026

12

Authors

86

  1. GN
    Guenter Neumann

    German Research Centre for Artificial Intelligence, Germany

    2 publications · 58 citations

    2

    h-index

  2. JV
    Josef van Genabith

    Dublin City University, Ireland

    2 publications · 58 citations

    2

    h-index

  3. MH
    Mans Hulden

    University of Colorado Boulder, United States

    2 publications · 38 citations

    2

    h-index

  4. GH
    Georg Heigold

    RWTH Aachen University, Germany

    2 publications · 58 citations

    2

    h-index

  5. SZ
    Shaoting Zhang

    Rutgers, The State University of New Jersey, United States

    1 publications · 16 citations

    1

    h-index

1–5 of 86

Venues

  1. Brain and Language

    2 publications · 210 citations

    2

    h-index

  2. Neuropeptides

    1 publications · 22 citations

    1

    h-index

  3. eScholarship (California Digital Library)

    Repository · 1 publications · 17 citations

    1

    h-index

  4. Real-Time Imaging

    1 publications · 36 citations

    1

    h-index

  5. Lab on a Chip

    1 publications · 21 citations

    1

    h-index

1–5