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It has been shown that named entity recognition (NER) could benefit from incorporating the long-distance structured information captured by dependency trees.
Dependency-aware named entity recognition with relative and global attentions
Gustavo Aguilar and Thamar Solorio. 2019 · 1909
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N. Chomsky. 1956 · 1956
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N. Chomsky. 1969 · 1969
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Long short-term memory
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John Lafferty, Andrew McCallum, and Fernando Pereira. 2001 · 2001
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Incorporating non-local information into information extraction systems by Gibbs sampling
Jenny Rose Finkel, Trond Grenager, and Christopher Manning. 2005 · 2005
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Stanford typed dependencies manual
Marie-Catherine De Marneffe and Christopher D Manning. 2008 · 2008
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Japanese named entity recognition using structural natural language processing
Ryohei Sasano and Sadao Kurohashi. 2008 · 2008
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Learning Deep Architectures for AI
Y. Bengio. 2009 · 2009
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Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2010 · 2010
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Recognizing biomedical named entities using skip-chain conditional random fields
Jingchen Liu, Minlie Huang, and Xiaoyan Zhu. 2010 · 2010
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Semeval-2010 task 1: Coreference resolution in multiple languages
Marta Recasens, Lluís Màrquez, Emili Sapena, M Antònia Martí, Mariona Taulé, Véronique Hoste, Massimo Poesio, and Yannick Versley. 2010 · 2010
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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 · 2013
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Ontonotes release 5.0 ldc2013t19
Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013 · 2013
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The Stanford CoreNLP natural language processing toolkit
Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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D. Sandra and M. Taft. 2014 · 2014
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Semantically conditioned lstm-based natural language generation for spoken dialogue systems
Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic, Pei hao Su, David Vandyke, and Steve J. Young. 2015 · 2015
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Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
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End-to-end sequence labeling via bi-directional lstm-cnns-crf
Xuezhe Ma and Eduard H. Hovy. 2016 · 2016
Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Ordered neurons: Integrating tree structures into recurrent neural networks
Yikang Shen, Shawn Tan, Alessandro Sordoni, and Aaron C. Courville. 2018 · 2018
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Neural segmental hypergraphs for overlapping mention recognition
Bailin Wang and Wei Lu. 2018 · 2018
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A neural transition-based model for nested mention recognition
Bailin Wang, Wei Lu, Yu Wang, and Hongxia Jin. 2018 · 2018
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bert-as-service
Han Xiao. 2018 · 2018
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Chinese NER using lattice LSTM
Yue Zhang and Jie Yang. 2018 · 2018
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Cited alongside, same era.
Deep biaffine attention for neural dependency parsing
Timothy Dozat and Christopher D Manning. 2017 · 2017
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Efficient dependency-guided named entity recognition
Zhanming Jie, Aldrian Obaja Muis, and Wei Lu. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2017 · 2017
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Leveraging linguistic structures for named entity recognition with bidirectional recursive neural networks
Peng-Hsuan Li, Ruo-Ping Dong, Yu-Siang Wang, Ju-Chieh Chou, and Wei-Yun Ma. 2017 · 2017
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Fast and accurate entity recognition with iterated dilated convolutions
Emma Strubell, Patrick Verga, David Belanger, and Andrew McCallum. 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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Robust lexical features for improved neural network named-entity recognition
Abbas Ghaddar and Phillippe Langlais. 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. 2019 · 2019
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Dependency-guided lstm-crf for named entity recognition
Zhanming Jie and Wei Lu. 2019 · 2019
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Chinese ner with height-limited constituent parsing
Rui Wang, Xin Xin, Wei Chang, Kun Ming, Biao Li, and Xin Fan. 2019 · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 2019
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Hierarchical contextualized representation for named entity recognition
Ying Luo, Fengshun Xiao, and Hai Zhao. 2020 · 2020
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Gábor Melis, Tomás Kociský, and Phil Blunsom. 2020 · 2020
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Named entity recognition as dependency parsing
Juntao Yu, Bernd Bohnet, and Massimo Poesio. 2020 · 2020
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Graph convolution over pruned dependency trees improves relation extraction
Yuhao Zhang, Peng Qi, and Christopher D. Manning. 2018b · 2080
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