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Sequence labeling (SL) is a fundamental research problem encompassing a variety of tasks, e.g., part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc.
Statistical inference for probabilistic functions of finite state markov chains
Leonard E Baum and Ted Petrie · 1966
Earlier work this paper cites.
Maximum-entropy models in science and engineering
Jagat Narain Kapur · 1989
Earlier work this paper cites.
Building a large annotated corpus of english: The penn treebank
Mitchell Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz · 1993
Earlier work this paper cites.
Hidden markov models
Sean R Eddy · 1996
Earlier work this paper cites.
A maximum entropy model for part-of-speech tagging
Adwait Ratnaparkhi · 1996
Earlier work this paper cites.
Support vector machines
Marti A. Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf · 1998
Earlier work this paper cites.
An algorithm that learns what’s in a name
Daniel M Bikel, Richard Schwartz, and Ralph M Weischedel · 1999
Earlier work this paper cites.
Use of support vector learning for chunk identification
Taku Kudoh and Yuji Matsumoto · 2000
Earlier work this paper cites.
Maximum entropy markov models for information extraction and segmentation
Andrew McCallum, Dayne Freitag, and Fernando CN Pereira · 2000
Earlier work this paper cites.
Introduction to the conll-2000 shared task: Chunking
Erik F Sang and Sabine Buchholz · 2000
Earlier work this paper cites.
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John Lafferty, Andrew McCallum, and Fernando CN Pereira · 2001
Earlier work this paper cites.
Named entity recognition: a maximum entropy approach using global information
Hai Leong Chieu and Hwee Tou Ng · 2002
Earlier work this paper cites.
Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms
Michael Collins · 2002
Earlier work this paper cites.
Efficient support vector classifiers for named entity recognition
Hideki Isozaki and Hideto Kazawa · 2002
Earlier work this paper cites.
Introduction to the conll-2002 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang · 2002
Earlier work this paper cites.
Named entity recognition using an hmm-based chunk tagger
GuoDong Zhou and Jian Su · 2002
Earlier work this paper cites.
Maximum entropy models for named entity recognition
Oliver Bender, Franz Josef Och, and Hermann Ney · 2003
Earlier work this paper cites.
Named entity recognition with long short-term memory
James Hammerton · 2003
Earlier work this paper cites.
Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons
Andrew McCallum and Wei Li · 2003
Earlier work this paper cites.
Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik F Sang and Fien De Meulder · 2003
Earlier work this paper cites.
Jacobian-free newton–krylov methods: a survey of approaches and applications
Dana A Knoll and David E Keyes · 2004
Earlier work this paper cites.
Svm based learning system for information extraction
Yaoyong Li, Kalina Bontcheva, and Hamish Cunningham · 2004
Earlier work this paper cites.
Deterministic dependency parsing of english text
Joakim Nivre and Mario Scholz · 2004
Earlier work this paper cites.
Semi-markov conditional random fields for information extraction
Sunita Sarawagi and William W Cohen · 2005
Earlier work this paper cites.
Ontonotes: The 90 \ \backslash % solution
Eduard Hovy, Mitchell Marcus, Martha Palmer, Lance Ramshaw, and Ralph Weischedel · 2006
Earlier work this paper cites.
An effective two-stage model for exploiting non-local dependencies in named entity recognition
Vijay Krishnan and Christopher D Manning · 2006
Earlier work this paper cites.
A survey of named entity recognition and classification
David Nadeau and Satoshi Sekine · 2007
Earlier work this paper cites.
Comparisons of sequence labeling algorithms and extensions
Nam Nguyen and Yunsong Guo · 2007
Earlier work this paper cites.
Part of speech taggers for morphologically rich indian languages: a survey
Dinesh Kumar and Gurpreet Singh Josan · 2010
Earlier work this paper cites.
Supervised noun phrase coreference research: The first fifteen years
V. Ng · 2010
Earlier work this paper cites.
Natural language processing (almost) from scratch
Ronan Collobert, Koray Kavukcuoglu, Jason Weston, Leon Bottou, Pavel Kuksa, and Michael Karlen · 2011
Earlier work this paper cites.
A universal part-of-speech tagset
Slav Petrov, Dipanjan Das, and Ryan McDonald · 2011
Earlier work this paper cites.
Named entity recognition in tweets: an experimental study
Alan Ritter, Sam Clark, Oren Etzioni, et al · 2011
Earlier work this paper cites.
An introduction to conditional random fields
Charles Sutton, Andrew McCallum, et al · 2012
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Towards robust linguistic analysis using ontonotes
Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong · 2013
Earlier work this paper cites.
On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
Earlier work this paper cites.
How to construct deep recurrent neural networks
Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
Earlier work this paper cites.
Learning character-level representations for part-of-speech tagging
Cicero Nogueira Dos Santos and Bianca Zadrozny · 2014
Earlier work this paper cites.
Bidirectional lstm-crf models for sequence tagging
Zhiheng Huang, Wei Xu, and Kai Yu · 2015
Earlier work this paper cites.
Segmental recurrent neural networks
Lingpeng Kong, Chris Dyer, and Noah A Smith · 2015
Earlier work this paper cites.
Finding function in form: Compositional character models for open vocabulary word representation
Wang Ling, Tiago Luís, Luís Marujo, Ramón Fernandez Astudillo, Silvio Amir, Chris Dyer, Alan W Black, and Isabel Trancoso · 2015
Earlier work this paper cites.
Not all contexts are created equal: Better word representations with variable attention
Wang Ling, Yulia Tsvetkov, Silvio Amir, Ramon Fermandez, Chris Dyer, Alan W Black, Isabel Trancoso, and Chu-Cheng Lin · 2015
Earlier work this paper cites.
Universal dependencies 1.2
Joakim Nivre, Željko Agić, Maria Jesus Aranzabe, Masayuki Asahara, Aitziber Atutxa, Miguel Ballesteros, John Bauer, Kepa Bengoetxea, Riyaz Ahmad Bhat, Cristina Bosco, et al · 2015
Earlier work this paper cites.
Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2015
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Answer sequence learning with neural networks for answer selection in community question answering
Xiaoqiang Zhou, Baotian Hu, Qingcai Chen, Buzhou Tang, and Xiaolong Wang · 2015
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Globally normalized transition-based neural networks
Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, and Michael Collins · 2016
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Named entity recognition with bidirectional lstm-cnns
Jason PC Chiu and Eric Nichols · 2016
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Robust lexical features for improved neural network named-entity recognition
Abbas Ghaddar and Philippe Langlais · 2018
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Constituent parsing as sequence labeling
Carlos Gómez-Rodríguez and David Vilares · 2018
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Named entity recognition with parallel recurrent neural networks
Andrej Zukov Gregoric, Yoram Bachrach, and Sam Coope · 2018
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Character-level supervision for low-resource pos tagging
Katharina Kann, Johannes Bjerva, Isabelle Augenstein, Barbara Plank, and Anders Søgaard · 2018
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Neural semi-markov conditional random fields for robust character-based part-of-speech tagging
Apostolos Kemos, Heike Adel, and Hinrich Schütze · 2018
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Structured prediction models for rnn based sequence labeling in clinical text
Abhyuday N Jagannatha and Hong Yu · 2016
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Charner: Character-level named entity recognition
Onur Kuru, Ozan Arkan Can, and Deniz Yuret · 2016
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Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer · 2016
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Dataset and neural recurrent sequence labeling model for open-domain factoid question answering
Peng Li, Wei Li, Zhengyan He, Xuguang Wang, Ying Cao, Jie Zhou, and Wei Xu · 2016
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A new recurrent neural crf for learning non-linear edge features
Shuming Ma and Xu Sun · 2016
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Jing Li, Aixin Sun, Jianglei Han, and Chenliang Li · 2018
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Segbot: A generic neural text segmentation model with pointer network
Jing Li, Aixin Sun, and Shafiq Joty · 2018
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Seq2seq dependency parsing
Zuchao Li, Jiaxun Cai, Shexia He, and Hai Zhao · 2018
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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 · 2018
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Emotionx-area66: Predicting emotions in dialogues using hierarchical attention network with sequence labeling
Rohit Saxena, Savita Bhat, and Niranjan Pedanekar · 2018
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Deep active learning for named entity recognition
Yanyao Shen, Hyokun Yun, Zachary C Lipton, Yakov Kronrod, and Animashree Anandkumar · 2018
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Deep semantic role labeling with self-attention
Zhixing Tan, Mingxuan Wang, Jun Xie, Yidong Chen, and Xiaodong Shi · 2018
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Embedded-state latent conditional random fields for sequence labeling
Dung Thai, Sree Harsha Ramesh, Shikhar Murty, Luke Vilnis, and Andrew McCallum · 2018
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Evaluating the utility of hand-crafted features in sequence labelling
Minghao Wu, Fei Liu, and Trevor Cohn · 2018
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Learning better internal structure of words for sequence labeling
Yingwei Xin, Ethan Hart, Vibhuti Mahajan, and Jean-David Ruvini · 2018
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A survey on recent advances in named entity recognition from deep learning models
Vikas Yadav and Steven Bethard · 2018
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Robust multilingual part-of-speech tagging via adversarial training
Michihiro Yasunaga, Jungo Kasai, and Dragomir Radev · 2018
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Hybrid semi-markov crf for neural sequence labeling
Zhi-Xiu Ye and Zhen-Hua Ling · 2018
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Learning tag dependencies for sequence tagging
Yuan Zhang, Hongshen Chen, Yihong Zhao, Qun Liu, and Dawei Yin · 2018
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Sentence-state lstm for text representation
Yue Zhang, Qi Liu, and Linfeng Song · 2018
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Sub-event detection from twitter streams as a sequence labeling problem
Giannis Bekoulis, Johannes Deleu, Thomas Demeester, and Chris Develder · 2019
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Grn: Gated relation network to enhance convolutional neural network for named entity recognition
Hui Chen, Zijia Lin, Guiguang Ding, Jianguang Lou, Yusen Zhang, and Borje Karlsson · 2019
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Hierarchically-refined label attention network for sequence labeling
Leyang Cui and Yue Zhang · 2019
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Complex word identification as a sequence labelling task
Sian Gooding and Ekaterina Kochmar · 2019
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Star-transformer
Qipeng Guo, Xipeng Qiu, Pengfei Liu, Yunfan Shao, Xiangyang Xue, and Zheng Zhang · 2019
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Neural crf transducers for sequence labeling
Kai Hu, Zhijian Ou, Min Hu, and Junlan Feng · 2019
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Improved differentiable architecture search for language modeling and named entity recognition
Yufan Jiang, Chi Hu, Tong Xiao, Chunliang Zhang, and Jingbo Zhu · 2019
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Gazetteer-enhanced attentive neural networks for named entity recognition
Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Bin Dong, and Shanshan Jiang · 2019
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Towards improving neural named entity recognition with gazetteers
Tianyu Liu, Jin-Ge Yao, and Chin-Yew Lin · 2019
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Gcdt: A global context enhanced deep transition architecture for sequence labeling
Yijin Liu, Fandong Meng, Jinchao Zhang, Jinan Xu, Yufeng Chen, and Jie Zhou · 2019
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Selectively connected self-attentions for semantic role labeling
Jaehui Park · 2019
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Viable dependency parsing as sequence labeling
Michalina Strzyz, David Vilares, and Carlos Gómez-Rodríguez · 2019
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Tener: Adapting transformer encoder for name entity recognition
Hang Yan, Bocao Deng, Xiaonan Li, and Xipeng Qiu · 2019
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Sendong Zhao, Ting Liu, Sicheng Zhao, and Fei Wang · 2019
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Can-ner: Convolutional attention network for chinese named entity recognition
Yuying Zhu, Guoxin Wang, and Börje F Karlsson · 2019
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Seqvat: Virtual adversarial training for semi-supervised sequence labeling
Luoxin Chen, Weitong Ruan, Xinyue Liu, and Jianhua Lu · 2020
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Knowledge-graph augmented word representations for named entity recognition
Qizhen He, Liang Wu, Yida Yin, and Heming Cai · 2020
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Generalizing natural language analysis through span-relation representations
Zhengbao Jiang, Wei Xu, Jun Araki, and Graham Neubig · 2020
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A unified mrc framework for named entity recognition
Xiaoya Li, Jingrong Feng, Yuxian Meng, Qinghong Han, Fei Wu, and Jiwei Li · 2020
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Handling rare entities for neural sequence labeling
Yangming Li, Han Li, Kaisheng Yao, and Xiaolong Li · 2020
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Hierarchical contextualized representation for named entity recognition
Ying Luo, Fengshun Xiao, and Hai Zhao · 2020
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Soft gazetteers for low-resource named entity recognition
Shruti Rijhwani, Shuyan Zhou, Graham Neubig, and Jaime Carbonell · 2020
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Position-aware self-attention based neural sequence labeling
Wei Wei, Zanbo Wang, Xianling Mao, Guangyou Zhou, Pan Zhou, and Sheng Jiang · 2020
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