Machine Learning-based Morphological Disambiguation

Machine Learning-Based Morphological Disambiguation is a computational method within natural language processing that employs machine learning algorithms to automatically resolve ambiguities in word morphology by selecting the most contextually appropriate morphological analysis from competing alternatives.

33

Publications

1.6K

Citations

81

Authors

48

Institutions

Publications per year

2017–2026

12

Authors

81

  1. NH
    Nizar Habash

    Columbia University, United States

    5 publications · 676 citations

    5

    h-index

  2. NZ
    Nasser Zalmout

    New York University Abu Dhabi, United Arab Emirates

    3 publications · 108 citations

    3

    h-index

  3. RC
    Ryan Cotterell

    Johns Hopkins University, United States

    2 publications · 45 citations

    2

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  4. SÜ
    Suzan Üsküdarlı

    Boğaziçi University, Türkiye

    2 publications · 25 citations

    2

    h-index

  5. RA
    Raja Ayed

    Manouba University, Tunisia

    2 publications · 43 citations

    2

    h-index

1–5 of 81

Venues

  1. International Conference on Computational Linguistics

    Conference · 2 publications · 51 citations

    2

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  2. arXiv (Cornell University)

    Repository · 2 publications · 25 citations

    2

    h-index

  3. Lexikos

    2 publications · 46 citations

    2

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  4. 1

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  5. Computer Speech & Language

    1 publications · 22 citations

    1

    h-index

1–5