Conditional Random Fields
Conditional Random Fields is a discriminative probabilistic model for structured prediction that specifies a conditional probability distribution over label sequences given an observation sequence, distinguished from generative models by directly modeling the conditional dependence of labels on observations without assuming independence among inputs.
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2017–2026
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Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2017 · 22.1K citations
Neural Architectures for Named Entity Recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian et al. · 2016 · 4.4K citations · Full text
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The University of Texas Health Science Center at Houston, United States
16 publications · 1.2K citations
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Carnegie Mellon University
Pittsburgh, United States
35 h-index
106 publications
43.5K citations

University of Massachusetts Amherst
Amherst Center, United States
32 h-index
100 publications
15.8K citations

Chinese Academy of Sciences
Beijing, China
30 h-index
189 publications
9.3K citations
Tsinghua University
Beijing, China
30 h-index
128 publications
9K citations

Harbin Institute of Technology
Harbin, China
29 h-index
166 publications
8.7K citations
Microsoft (United States)
Redmond, United States
28 h-index
80 publications
10.2K citations
Stanford University
Stanford, United States
27 h-index
79 publications
26.5K citations
Microsoft Research Asia (China)
Beijing, China
26 h-index
79 publications
15.8K citations
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Repository · 113 publications · 16.5K citations
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TPAMI · 40 publications · 31K citations
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