Maximum Entropy Tagging

Maximum Entropy Tagging is a probabilistic part-of-speech tagging method that, as a discriminative model, distinguishes itself from other tagging approaches by employing the maximum entropy principle to integrate diverse and overlapping features while estimating the most uncertain (i.e., maximally entropic) probability distribution consistent with empirical data.

70

Publications

9.8K

Citations

158

Authors

54

Institutions

Publications per year

2017–2026

2

Variants

Maxent Tagging

Authors

158

  1. SB
    Sivaji Bandyopadhyay

    Jadavpur University, India

    3 publications · 80 citations

    3

    h-index

  2. JZ
    Jakub Zavrel

    Tilburg University, Netherlands

    3 publications · 299 citations

    3

    h-index

  3. AE
    Asif Ekbal

    Indian Institute of Technology Patna, India

    3 publications · 80 citations

    3

    h-index

  4. HV
    Hans van Halteren

    Radboud University Nijmegen, Netherlands

    3 publications · 299 citations

    3

    h-index

  5. WD
    Walter Daelemans

    University of Antwerp, Belgium

    3 publications · 299 citations

    3

    h-index

1–5 of 158

Venues

  1. ACM Transactions on Asian Language Information Processing

    TALIP · 2 publications · 35 citations

    2

    h-index

  2. 2

    h-index

  3. DSpace repository (University of Tartu)

    Repository · 1 publications · 10 citations

    1

    h-index

  4. NLPRS

    1 publications · 21 citations

    1

    h-index

  5. 1

    h-index

1–5