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
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158
Authors
54
Institutions
Publications per year
2017–2026
2
Variants
Maxent Tagging
Parents
70
Discriminative training methods for hidden Markov models
Michael Collins · 2002 · 1.9K citations · Full text
Thorsten Brants · 2000 · 1.3K citations · Full text
A Maximum Entropy Model for Part-Of-Speech Tagging
Adwait Ratnaparkhi · 1996 · 1.3K citations
A maximum entropy approach to identifying sentence boundaries
Jeffrey C. Reynar, Adwait Ratnaparkhi · 1997 · 393 citations · Full text
Computing, Computer Science, English +17
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IBM Research - Thomas J. Watson Research Center
Yorktown Heights, United States
5 h-index
17 publications
736 citations
University of Pennsylvania
Philadelphia, United States
5 h-index
9 publications
2K citations

Jadavpur University
Kolkata, India
3 h-index
8 publications
230 citations

University of Edinburgh
Edinburgh, United Kingdom
3 h-index
6 publications
772 citations
Stanford University
Stanford, United States
3 h-index
6 publications
2.3K citations

Radboud University Nijmegen
Nijmegen, The Netherlands
3 h-index
3 publications
299 citations
Indian Institute of Technology Kharagpur
Kharagpur, India
2 h-index
5 publications
237 citations

Tilburg University
Tilburg, The Netherlands
2 h-index
4 publications
226 citations
1–8 of 54
TALIP · 2 publications · 35 citations
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2 publications · 61 citations
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Repository · 1 publications · 10 citations
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1 publications · 21 citations
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1 publications · 29 citations
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