Fetching the paper…
Reading the bibliography…
While part-of-speech (POS) tagging and dependency parsing are observed to be closely related, existing work on joint modeling with manually crafted feature templates suffers from the feature sparsity and incompleteness problems.
Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms
Michael Collins · 2002
Earlier work this paper cites.
Feature-rich part-of-speech tagging with a cyclic dependency network
Kristina Toutanova, Dan Klein, Christopher D. Manning, and Yoram Singer · 2003
Earlier work this paper cites.
Online large-margin training of dependency parsers
Ryan McDonald, Koby Crammer, and Fernando Pereira · 2005
Earlier work this paper cites.
Generating typed dependency parses from phrase structure parses
Marie-Catherine de Marneffe, Bill MacCartney, and Christopher D. Manning · 2006
Earlier work this paper cites.
Labeled pseudo-projective dependency parsing with support vector machines
Joakim Nivre, Johan Hall, Jens Nilsson, Gulsen Eryigit, and Svetoslav Marinov · 2006
Earlier work this paper cites.
Algorithms for deterministic incremental depdendency parsing
Joakim Nivre · 2008
Earlier work this paper cites.
A tale of two parsers: Investigating and combining graph-based and transition-based dependency parsing
Yue Zhang and Stephen Clark · 2008
Earlier work this paper cites.
Incremental joint pos tagging and dependency parsing in chinese
Jun Hatori, Takuya Matsuzaki, Yusuke Miyao, and Jun’ichi Tsujii · 2011
Earlier work this paper cites.
Joint models for chinese pos tagging and dependency parsing
Zhenghua Li, Min Zhang, Wanxiang Che, Ting Liu, Wenliang Chen, and Haizhou Li · 2011
Earlier work this paper cites.
A transition-based system for joint part-of-speech tagging and labeled non-projective parsing
Bernd Bohnet and Joakim Nivre · 2012
Earlier work this paper cites.
A separately passive-aggressive training algorithm for joint pos tagging and dependency parsing
Zhenghua Li, Min Zhang, Wanxiang Che, and Ting Liu · 2012
Cited alongside, same era.
Stacking heterogeneous joint models of Chinese POS tagging and dependency parsing
Meishan Zhang, Wanxiang Che, Ting Liu, and Zhenghua Li · 2012
Cited alongside, same era.
On the difficulty of training recurrent neural networks
R. Pascanu, T. Mikolov, and Y. Bengio · 2013
Cited alongside, same era.
Capturing long-distance dependencies in sequence models: A case study of chinese part-of-speech tagging
Weiwei Sun, Xiaochang Peng, and Xiaojun Wan · 2013
Cited alongside, same era.
A fast and accurate dependency parser using neural networks
Danqi Chen and Christopher D. Manning · 2014
Cited alongside, same era.
Learning character-level representations for part-of-speech tagging
Bidirectional lstm-crf models for sequence tagging
Zhiheng Huang, Wei Xu, and Kai Yu · 2015
Later among the works it cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Later among the works it cites.
Many languages, one parser
Waleed Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, and Noah A. Smith · 2016
Later among the works it cites.
Globally normalized transition-based neural networks
Daniel Andor, Chris Alberti, David Weiss, Aliasei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, and Michael Collins · 2016
Later among the works it cites.
Bi-directional attention with agreement for dependency parsing
Hao Cheng, Hao Fang, Xiaodong He, Jianfeng Gao, and Li Deng · 2016
Later among the works it cites.
Simple and accurate dependency parsing using bidirectional lstm feature representations
Eliyahu Kiperwasser and Yoav Goldberg · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cícero Nogueira dos Santos and Bianca Zadrozny · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhtdinov · 2014
Cited alongside, same era.
Improved transition-based parsing and tagging with neural networks
Chris Alberti, David Weiss, Greg Coppola, and Slav Petrov · 2015
Cited alongside, same era.
Improved transition-based parsing by modeling characters instead of words with lstms
Miguel Ballesteros, Chris Dyer, and Noah Smith · 2015
Cited alongside, same era.
Transition-based depdnency parsing with stack long short-term memory
Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Mattews, and A. Smith, Noah · 2015
Cited alongside, same era.
Later among the works it cites.
Graph-based dependency parsing with bidirectional lstm
Wenhui Wang and Baobao Chang · 2016
Later among the works it cites.
Stack-propagation: Improved representation learning for syntax
Yuan Zhang and David Weiss · 2016
Later among the works it cites.
Deep biaffine attention for neural dependency parsing
Timothy Dozat and Christopher D. Manning · 2017
Closest in time.