Supervised Morphological Analysis

Supervised Morphological Analysis is a machine learning approach to morphological parsing in which a model is trained on annotated data containing explicit morpheme segmentations and labels, distinguishing it from unsupervised or semi-supervised methods by relying on human-annotated examples to learn the mapping from raw text to morphological structure.

15

Publications

1K

Citations

45

Authors

18

Institutions

Publications per year

2017–2026

2

Authors

45

  1. MN
    Matteo Negri

    Fondazione Bruno Kessler, Italy

    2 publications · 138 citations

    2

    h-index

  2. DA
    Duygu Ataman

    New York University, United States

    2 publications · 138 citations

    2

    h-index

  3. MT
    Marco Turchi

    Fondazione Bruno Kessler, Italy

    2 publications · 138 citations

    2

    h-index

  4. MF
    Marcello Federico

    Fondazione Bruno Kessler, Italy

    2 publications · 138 citations

    2

    h-index

  5. AF
    Agnieszka Faleńska

    University of Stuttgart, Germany

    1 publications · 16 citations

    1

    h-index

1–5 of 45

Venues

  1. Empirical Methods in Natural Language Processing

    Conference · 1 publications · 23 citations

    1

    h-index

  2. International Journal of Speech Technology

    1 publications · 11 citations

    1

    h-index

  3. 1

    h-index

  4. DOAJ (DOAJ: Directory of Open Access Journals)

    Repository · 1 publications · 125 citations

    1

    h-index

  5. Lund University Publications (Lund University)

    Repository · 1 publications · 16 citations

    1

    h-index

1–5