Statistical Dependency Parsing

Statistical Dependency Parsing is a computational method within dependency grammar that identifies syntactic dependencies between words in a sentence using probabilistic models trained on annotated corpora to predict the most likely dependency structure.

108

Publications

8.2K

Citations

259

Authors

98

Institutions

Publications per year

2017–2026

5

Authors

259

  1. JN
    Joakim Nivre

    Uppsala University, Sweden

    7 publications · 399 citations

    7

    h-index

  2. NA
    Noah A. Smith

    Carnegie Mellon University, United States

    5 publications · 415 citations

    5

    h-index

  3. MC
    Michael Collins

    University of Calgary, Canada

    4 publications · 1.6K citations

    4

    h-index

  4. MS
    Mark Steedman

    University of Edinburgh, United Kingdom

    4 publications · 734 citations

    4

    h-index

  5. JH
    Julia Hockenmaier

    University of Illinois Urbana-Champaign, United States

    4 publications · 707 citations

    4

    h-index

1–5 of 259

Venues

  1. Computational Linguistics

    4 publications · 533 citations

    4

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 65 citations

    3

    h-index

  3. 2

    h-index

  4. Arrow@dit (Dublin Institute of Technology)

    Repository · 2 publications · 184 citations

    2

    h-index

  5. Empirical Methods in Natural Language Processing

    Conference · 1 publications · 58 citations

    1

    h-index

1–5