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Íslensk orðtíðnibók [the icelandic frequency dictionary]
Jörgen Pind, Friðrik Magnússon, and Stefán Briem. 1991 · 1991
Earlier work this paper cites.
A study of cross-validation and bootstrap for accuracy estimation and model selection
Ron Kohavi. 1995 · 1995
Earlier work this paper cites.
Boosting applied to tagging and pp attachment
Steven Abney, Robert E Schapire, and Yoram Singer. 1999 · 1999
Earlier work this paper cites.
A short introduction to boosting
Yoav Freund, Robert Schapire, and Naoki Abe. 1999 · 1999
Earlier work this paper cites.
Improved boosting algorithms using confidence-rated predictions
Robert E Schapire and Yoram Singer. 1999 · 1999
Earlier work this paper cites.
Detecting errors in corpora using support vector machines
Tetsuji Nakagawa and Yuji Matsumoto. 2002 · 2002
Earlier work this paper cites.
Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
Earlier work this paper cites.
Efficient graph-based semi-supervised learning of structured tagging models
Amarnag Subramanya, Slav Petrov, and Fernando C. N. Pereira. 2010 · 2010
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Part-of-speech tagging from 97% to 100%: Is it time for some linguistics?
Christopher D. Manning. 2011 · 2011
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Correcting errors in a new gold standard for tagging icelandic text
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Neural architectures for named entity recognition
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End-to-end sequence labeling via bi-directional lstm-cnns-crf
Xuezhe Ma and Eduard H. Hovy. 2016 · 2016
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Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power. 2017 · 2017
Detecting annotation noise in automatically labelled data
Ines Rehbein and Josef Ruppenhofer. 2017 · 2017
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Contextual string embeddings for sequence labeling
Alan Akbik, Duncan Blythe, and Roland Vollgraf. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Later among the works it cites.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Pooled contextualized embeddings for named entity recognition
Alan Akbik, Tanja Bergmann, and Roland Vollgraf. 2019 · 2019
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Learning named entity tagger using domain-specific dictionary
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Cited alongside, same era.
Efficient contextualized representation: Language model pruning for sequence labeling
Liyuan Liu, Xiang Ren, Jingbo Shang, Xiaotao Gu, Jian Peng, and Jiawei Han. 2018a
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Empower sequence labeling with task-aware neural language model
Liyuan Liu, Jingbo Shang, Xiang Ren, Frank Fangzheng Xu, Huan Gui, Jian Peng, and Jiawei Han. 2018b
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Jingbo Shang, Liyuan Liu, Xiaotao Gu, Xiang Ren, Teng Ren, and Jiawei Han. 2018 · 2064
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