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Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER).
An analysis of approximations for maximizing submodular set functions—i
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
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Support vector machine active learning with applications to text classification
Simon Tong and Daphne Koller · 2001
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English gigaword, ldc catalog no
David Graff and Christopher Cieri · 2003
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik F Tjong Kim Sang and Fien De Meulder · 2003
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Multi-criteria-based active learning for named entity recognition
Dan Shen, Jie Zhang, Jian Su, Guodong Zhou, and Chew-Lim Tan · 2004
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Reducing labeling effort for structured prediction tasks
Aron Culotta and Andrew McCallum · 2005
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Analysis of perceptron-based active learning
Sanjoy Dasgupta, Adam Tauman Kalai, and Claire Monteleoni · 2005
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Cost-effective outbreak detection in networks
Jure Leskovec, Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne VanBriesen, and Natalie Glance · 2007
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An analysis of active learning strategies for sequence labeling tasks
Burr Settles and Mark Craven · 2008
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Agnostic active learning
Maria-Florina Balcan, Alina Beygelzimer, and John Langford · 2009
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Importance weighted active learning
Alina Beygelzimer, Sanjoy Dasgupta, and John Langford · 2009
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A literature survey of active machine learning in the context of natural language processing
Fredrik Olsson · 2009
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Active learning literature survey
Burr Settles · 2010
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Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa · 2011
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al · 2012
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Submodular function maximization
Andreas Krause and Daniel Golovin · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Deep gaussian processes
Andreas Damianou and Neil Lawrence · 2013
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Named entity recognition with bidirectional lstm-cnns
Jason PC Chiu and Eric Nichols · 2016
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A theoretically grounded application of dropout in recurrent neural networks
Yarin Gal and Zoubin Ghahramani · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Annotating named entities in consumer health questions
Halil Kilicoglu, Asma Ben Abacha, Yassine Mrabet, Kirk Roberts, Laritza Rodriguez, Sonya E Shooshan, and Dina Demner-Fushman · 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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Computational linguistics and deep learning
Christopher D Manning · 2016
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Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding
Grégoire Mesnil, Xiaodong He, Li Deng, and Yoshua Bengio · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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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
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The power of localization for efficiently learning linear separators with noise
Pranjal Awasthi, Maria Florina Balcan, and Philip M Long · 2014
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Streaming submodular maximization: Massive data summarization on the fly
Ashwinkumar Badanidiyuru, Baharan Mirzasoleiman, Amin Karbasi, and Andreas Krause · 2014
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Convolutional neural networks for sentence classification
Yoon Kim · 2014
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Toward mention detection robustness with recurrent neural networks
Thien Huu Nguyen, Avirup Sil, Georgiana Dinu, and Radu Florian · 2016
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Cost-effective active learning for deep image classification
Keze Wang, Dongyu Zhang, Ya Li, Ruimao Zhang, and Liang Lin · 2016
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Multi-task cross-lingual sequence tagging from scratch
Zhilin Yang, Ruslan Salakhutdinov, and William Cohen · 2016
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Crowdsourcing named entity recognition and entity linking corpora
Kalina Bontcheva, Leon Derczynski, and Ian Roberts · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Reporting score distributions makes a difference: Performance study of lstm-networks for sequence tagging
Nils Reimers and Iryna Gurevych · 2017
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Fast and accurate entity recognition with iterated dilated convolutions
Emma Strubell, Patrick Verga, David Belanger, and Andrew McCallum · 2017
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Revisiting perceptron: Efficient and label-optimal active learning of halfspaces
Songbai Yan and Chicheng Zhang · 2017
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Neural models for sequence chunking
Feifei Zhai, Saloni Potdar, Bing Xiang, and Bowen Zhou · 2017
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Active discriminative text representation learning
Ye Zhang, Matthew Lease, and Byron C Wallace · 2017
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