Semi-supervised Morphological Analysis

Semi-Supervised Morphological Analysis is a morphological parsing methodology that belongs to the class of semi-supervised learning approaches in natural language processing, distinguished by its utilization of a small amount of manually annotated morphological data alongside a larger body of unannotated text to induce morphological structure and relationships in a computationally efficient and linguistically informed manner.

14

Publications

489

Citations

32

Authors

12

Institutions

Publications per year

2017–2026

3

Authors

32

  1. SV
    Sámi Virpioja

    Aalto University, Finland

    7 publications · 388 citations

    7

    h-index

  2. OK
    Oskar Kohonen

    Aalto University, Finland

    5 publications · 207 citations

    5

    h-index

  3. MK
    Mikko Kurimo

    Aalto University, Finland

    5 publications · 317 citations

    5

    h-index

  4. PS
    Peter Smit

    Aalto University, Finland

    2 publications · 181 citations

    2

    h-index

  5. KL
    Krista Lagus

    University of Helsinki, Finland

    2 publications · 71 citations

    2

    h-index

1–5 of 32

Venues

  1. Lund University Publications (Lund University)

    Repository · 1 publications · 16 citations

    1

    h-index

  2. International Conference on Computational Linguistics

    Conference · 1 publications · 76 citations

    1

    h-index

  3. Computer Speech & Language

    1 publications · 13 citations

    1

    h-index

  4. Journal of Intelligent & Fuzzy Systems

    1 publications · 20 citations

    1

    h-index

  5. 1

    h-index

1–5