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A number of differences have emerged between modern and classic approaches to constituency parsing in recent years, with structural components like grammars and feature-rich lexicons becoming less central while recurrent neural network representations rise in popularity.
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Michael Collins. 1997 · 1997
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John Lafferty, Andrew McCallum, and Fernando CN Pereira. 2001 · 2001
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Dan Klein and Christopher D. Manning. 2003 · 2003
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Slav Petrov and Dan Klein. 2007 · 2007
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Jenny Rose Finkel, Alex Kleeman, and Christopher D. Manning. 2008 · 2008
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Structure compilation: trading structure for features
Percy Liang, Hal Daumé III, and Dan Klein. 2008 · 2008
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Efficient third-order dependency parsers
Terry Koo and Michael Collins. 2010 · 2010
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Less grammar, more features
David Hall, Greg Durrett, and Dan Klein. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
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Improved transition-based parsing by modeling characters instead of words with lstms
Miguel Ballesteros, Chris Dyer, and Noah A. Smith. 2015 · 2015
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Greg Durrett and Dan Klein. 2015 · 2015
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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. 2015a · 2015
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Grammar as a foreign language
Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, and Geoffrey Hinton. 2015 · 2015
Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
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Assessing the ability of lstms to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016 · 2016
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Does string-based neural mt learn source syntax?
Xing Shi, Inkit Padhi, and Kevin Knight. 2016 · 2016
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Graph-based dependency parsing with bidirectional lstm
Wenhui Wang and Baobao Chang. 2016 · 2016
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Kushal Chawla, Sunil Kumar Sahu, and Ashish Anand. 2017 · 2017
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N-gram language modeling using recurrent neural network estimation
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Cited alongside, same era.
Parsing as language modeling
Do Kook Choe and Eugene Charniak. 2016 · 2016
Cited alongside, same era.
Read, tag, and parse all at once, or fully-neural dependency parsing
Jan Chorowski, Michał Zapotoczny, and Paweł Rychlikowski. 2016 · 2016
Cited alongside, same era.
Span-based constituency parsing with a structure-label system and provably optimal dynamic oracles
James Cross and Liang Huang. 2016b · 2016
Cited alongside, same era.
Recurrent neural network grammars
Chris Dyer, Adhiguna Kuncoro, Miguel Ballesteros, and Noah A. Smith. 2016 · 2016
Cited alongside, same era.
Character-aware neural language models
Yoon Kim, Yacine Jernite, David Sontag, and Alexander M Rush. 2016 · 2016
Cited alongside, same era.
Character-based neural machine translation
Wang Ling, Isabel Trancoso, Chris Dyer, and Alan W Black. 2015b
Cited in the paper.
Ciprian Chelba, Mohammad Norouzi, and Samy Bengio. 2017 · 2017
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Shift-reduce constituent parsing with neural lookahead features
Jiangming Liu and Yue Zhang. 2017 · 2017
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Dynet: The dynamic neural network toolkit
Graham Neubig, Chris Dyer, Yoav Goldberg, Austin Matthews, Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, David Chiang, Daniel Clothiaux, Trevor Cohn, Kevin Duh, Manaal Faruqui, Cynthia Gan, Dan Garrette, Yangfeng Ji, Lingpeng Kong, Adhiguna Kuncoro, Gaurav Kumar, Chaitanya Malaviya, Paul Michel, Yusuke Oda, Matthew Richardson, Naomi Saphra, Swabha Swayamdipta, and Pengcheng Yin. 2017 · 2017
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A minimal span-based neural constituency parser
Mitchell Stern, Jacob Andreas, and Dan Klein. 2017 · 2017
Later among the works it cites.
Dependency parsing as head selection
Xingxing Zhang, Jianpeng Cheng, and Mirella Lapata. 2017 · 2017
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Multilingual lexicalized constituency parsing with word-level auxiliary tasks
Maximin Coavoux and Benoit Crabbé. 2017 · 2053
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