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We study the problem of analyzing tweets with Universal Dependencies.
Class-based n-gram models of natural language
Peter F. Brown, Peter V. deSouza, Robert L. Mercer, Vincent J. Della Pietra, and Jenifer C. Lai. 1992 · 1992
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Building a large annotated corpus of English: The Penn Treebank
Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993 · 1993
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An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
Thomas G. Dietterich. 2000 · 2000
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Statistical dependency analysis with support vector machines
Hiroyasu Yamada and Yuji Matsumoto. 2003 · 2003
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TweetMotif: Exploratory search and topic summarization for Twitter
Brendan O’Connor, Michel Krieger, and David Ahn. 2010 · 2010
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#hardtoparse: POS tagging and parsing the Twitterverse
Jennifer Foster, Özlem Çetinoǧlu, Joachim Wagner, Joseph Le Roux, Stephen Hogan, Joakim Nivre, Deirdre Hogan, and Josef Van Genabith. 2011 · 2011
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Part-of-speech tagging for twitter: Annotation, features, and experiments
Kevin Gimpel, Nathan Schneider, Brendan O’Connor, Dipanjan Das, Daniel Mills, Jacob Eisenstein, Michael Heilman, Dani Yogatama, Jeffrey Flanigan, and Noah A. Smith. 2011 · 2011
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Named entity recognition in tweets: An experimental study
Alan Ritter, Sam Clark, Mausam, and Oren Etzioni. 2011 · 2011
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A dynamic oracle for arc-eager dependency parsing
Yoav Goldberg and Joakim Nivre. 2012 · 2012
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A universal part-of-speech tagset
Slav Petrov, Dipanjan Das, and Ryan McDonald. 2012 · 2012
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Twitter part-of-speech tagging for all: Overcoming sparse and noisy data
Leon Derczynski, Alan Ritter, Sam Clark, and Kalina Bontcheva. 2013 · 2013
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What to do about bad language on the internet
Jacob Eisenstein. 2013 · 2013
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Collaborative dependency annotation
Kim Gerdes. 2013 · 2013
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Training deterministic parsers with non-deterministic oracles
Yoav Goldberg and Joakim Nivre. 2013 · 2013
Cited alongside, same era.
Improved part-of-speech tagging for online conversational text with word clusters
Olutobi Owoputi, Brendan O’Connor, Chris Dyer, Kevin Gimpel, Nathan Schneider, and Noah A. Smith. 2013 · 2013
Cited alongside, same era.
A framework for (under)specifying dependency syntax without overloading annotators
Nathan Schneider, Brendan O’Connor, Naomi Saphra, David Bamman, Manaal Faruqui, Noah A. Smith, Chris Dyer, and Jason Baldridge. 2013 · 2013
Cited alongside, same era.
Universal Stanford Dependencies: A cross-linguistic typology
Marie-Catherine de Marneffe, Timothy Dozat, Natalia Silveira, Katri Haverinen, Filip Ginter, Joakim Nivre, and Christopher D. Manning. 2014 · 2014
Cited alongside, same era.
Classification in the presence of label noise: A survey
Benoît Frénay and Michel Verleysen. 2014 · 2014
Cited alongside, same era.
Training with exploration improves a greedy stack LSTM parser
Miguel Ballesteros, Yoav Goldberg, Chris Dyer, and Noah A. Smith. 2016 · 2016
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Sequence-level knowledge distillation
Yoon Kim and Alexander M. Rush. 2016 · 2016
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Simple and accurate dependency parsing using bidirectional LSTM feature representations
Eliyahu Kiperwasser and Yoav Goldberg. 2016 · 2016
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Distilling an ensemble of greedy dependency parsers into one MST parser
Adhiguna Kuncoro, Miguel Ballesteros, Lingpeng Kong, Chris Dyer, and Noah A. Smith. 2016 · 2016
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End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
Xuezhe Ma and Eduard Hovy. 2016 · 2016
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Universal Dependencies v1: A multilingual treebank collection
Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter, Yoav Goldberg, Jan Hajic, Christopher D. Manning, Ryan McDonald, Slav Petrov, Sampo Pyysalo, Natalia Silveira, Reut Tsarfaty, and Daniel Zeman. 2016 · 2016
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Lingpeng Kong, Nathan Schneider, Swabha Swayamdipta, Archna Bhatia, Chris Dyer, and Noah A. Smith. 2014 · 2014
Cited alongside, same era.
The Stanford CoreNLP natural language processing toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014 · 2014
Cited alongside, same era.
GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Cited alongside, same era.
Improved transition-based parsing by modeling characters instead of words with LSTMs
Miguel Ballesteros, Chris Dyer, and Noah A. Smith. 2015 · 2015
Cited alongside, same era.
Cross-lingual dependency parsing based on distributed representations
Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, and Ting Liu. 2015 · 2015
Cited alongside, same era.
Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean. 2015 · 2015
Cited alongside, same era.
Bidirectional LSTM-CRF models for sequence tagging
Zhiheng Huang, Wei Xu, and Kai Yu. 2015 · 2015
Cited alongside, same era.
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Stanford’s graph-based neural dependency parser at the CoNLL 2017 shared task
Timothy Dozat, Peng Qi, and Christopher D. Manning. 2017 · 2017
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Part-of-speech tagging for Twitter with adversarial neural networks
Tao Gui, Qi Zhang, Haoran Huang, Minlong Peng, and Xuanjing Huang. 2017 · 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 · 2017
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Annotating Italian social media texts in Universal Dependencies
Manuela Sanguinetti, Cristina Bosco, Alessandro Mazzei, Alberto Lavelli, and Fabio Tamburini. 2017 · 2017
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Tokenizing, POS tagging, lemmatizing and parsing UD 2.0 with UDPipe
Milan Straka and Jana Straková. 2017 · 2017
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Universal Dependencies parsing for colloquial Singaporean English
Hongmin Wang, Yue Zhang, GuangYong Leonard Chan, Jie Yang, and Hai Leong Chieu. 2017 · 2017
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