Fetching the paper…
Reading the bibliography…
This paper describes NCRF++, a toolkit for neural sequence labeling.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
Andrew Viterbi. 1967 · 1967
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. 1989 · 1989
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Introduction to the conll-2000 shared task: Chunking
Erik F Tjong Kim Sang and Sabine Buchholz. 2000 · 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 · 2001
Earlier work this paper cites.
Shallow parsing with conditional random fields
Fei Sha and Fernando Pereira. 2003 · 2003
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.
Flexcrfs: Flexible conditional random fields
Xuan-Hieu Phan, Le-Minh Nguyen, and Cam-Tu Nguyen. 2004 · 2004
Earlier work this paper cites.
Crf versus svm-struct for sequence labeling
S Sathiya Keerthi and Sellamanickam Sundararajan. 2007 · 2007
Cited alongside, same era.
Crfsuite: a fast implementation of conditional random fields (crfs)
Naoaki Okazaki. 2007 · 2007
Cited alongside, same era.
Design challenges and misconceptions in named entity recognition
Lev Ratinov and Dan Roth. 2009 · 2009
Cited alongside, same era.
Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011 · 2011
Cited alongside, same era.
Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011 · 2011
Cited alongside, same era.
Bidirectional LSTM-CRF models for sequence tagging
Zhiheng Huang, Wei Xu, and Kai Yu. 2015 · 2015
Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
Later among the works it cites.
End-to-end sequence labeling via Bi-directional LSTM-CNNs-CRF
Xuezhe Ma and Eduard Hovy. 2016 · 2016
Later among the works it cites.
Semi-supervised sequence tagging with bidirectional language models
Matthew Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power. 2017 · 2017
Later among the works it cites.
Reporting score distributions makes a difference: Performance study of lstm-networks for sequence tagging
Nils Reimers and Iryna Gurevych. 2017 · 2017
Later among the works it cites.
Fast and accurate entity recognition with iterated dilated convolutions
Emma Strubell, Patrick Verga, David Belanger, and Andrew McCallum. 2017 · 2017
Later among the works it cites.
Transfer learning for sequence tagging with hierarchical recurrent networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy. 2015 · 2015
Cited alongside, same era.
Finding function in form: Compositional character models for open vocabulary word representation
Wang Ling, Chris Dyer, Alan W Black, Isabel Trancoso, Ramon Fermandez, Silvio Amir, Luis Marujo, and Tiago Luis. 2015 · 2015
Cited alongside, same era.
Named entity recognition with bidirectional LSTM-CNNs
Jason Chiu and Eric Nichols. 2016 · 2016
Cited alongside, same era.
Zhilin Yang, Ruslan Salakhutdinov, and William W Cohen. 2017 · 2017
Later among the works it cites.
Empower sequence labeling with task-aware neural language model
Liyuan Liu, Jingbo Shang, Frank Xu, Xiang Ren, Huan Gui, Jian Peng, and Jiawei Han. 2018 · 2018
Closest in time.
Design challenges and misconceptions in neural sequence labeling
Jie Yang, Shuailong Liang, and Yue Zhang. 2018 · 2018
Closest in time.