Fetching the paper…
Reading the bibliography…
A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Chinese word segmentation: A decade review
Chang-ning Huang and Hai Zhao. 2007 · 2007
Earlier work this paper cites.
Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011 · 2011
Earlier work this paper cites.
Improving chinese word segmentation and pos tagging with semi-supervised methods using large auto-analyzed data
Yiou Wang, Jun’ichi Kazama, Yoshimasa Tsuruoka, Wenliang Chen, Yujie Zhang, and Kentaro Torisawa. 2011 · 2011
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
Deep learning for chinese word segmentation and pos tagging
Xiaoqing Zheng, Hanyang Chen, and Tianyu Xu. 2013 · 2013
Earlier work this paper cites.
Max-margin tensor neural network for chinese word segmentation
Wenzhe Pei, Tao Ge, and Baobao Chang. 2014 · 2014
Earlier work this paper cites.
Recurrent neural network regularization
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014 · 2014
Earlier work this paper cites.
Long short-term memory neural networks for chinese word segmentation
Xinchi Chen, Xipeng Qiu, Chenxi Zhu, Pengfei Liu, and Xuanjing Huang. 2015 · 2015
Earlier work this paper cites.
Two/too simple adaptations of word2vec for syntax problems
Wang Ling, Chris Dyer, Alan W Black, and Isabel Trancoso. 2015 · 2015
Cited alongside, same era.
Accurate linear-time chinese word segmentation via embedding matching
Jianqiang Ma and Erhard Hinrichs. 2015 · 2015
Cited alongside, same era.
Structured training for neural network transition-based parsing
David Weiss, Chris Alberti, Michael Collins, and Slav Petrov. 2015 · 2015
Cited alongside, same era.
A theoretically grounded application of dropout in recurrent neural networks
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
Cited alongside, same era.
Exploring segment representations for neural segmentation models
Yijia Liu, Wanxiang Che, Jiang Guo, Bing Qin, and Ting Liu. 2016 · 2016
Cited alongside, same era.
Transition-based neural word segmentation
Neural joint model for transition-based chinese syntactic analysis
Shuhei Kurita, Daisuke Kawahara, and Sadao Kurohashi. 2017 · 2017
Later among the works it cites.
A non-dnn feature engineering approach to dependency parsing – fbaml at conll 2017 shared task
Xian Qian and Yang Liu. 2017 · 2017
Later among the works it cites.
Convolutional neural network with word embeddings for chinese word segmentation
Chunqi Wang and Bo Xu. 2017 · 2017
Later among the works it cites.
Neural word segmentation with rich pretraining
Jie Yang, Yue Zhang, and Fei Dong. 2017 · 2017
Later among the works it cites.
Conll 2017 shared task: Multilingual parsing from raw text to universal dependencies
Daniel Zeman, Martin Popel, Milan Straka, Jan Hajic, Joakim Nivre, Filip Ginter, Juhani Luotolahti, Sampo Pyysalo, Slav Petrov, Martin Potthast, Francis Tyers, Elena Badmaeva, Memduh Gokirmak, Anna Nedoluzhko, Silvie Cinkova, Jan Hajic jr., Jaroslava Hlavacova, Václava Kettnerová, Zdenka Uresova, Jenna Kanerva, Stina Ojala, Anna Missilä, Christopher D. Manning, Sebastian Schuster, Siva Reddy, Dima Taji, Nizar Habash, Herman Leung, Marie-Catherine de Marneffe, Manuela Sanguinetti, Maria Simi, Hiroshi Kanayama, Valeria dePaiva, Kira Droganova, Héctor Martínez Alonso, Çağrı Çöltekin, Umut Sulubacak, Hans Uszkoreit, Vivien Macketanz, Aljoscha Burchardt, Kim Harris, Katrin Marheinecke, Georg Rehm, Tolga Kayadelen, Mohammed Attia, Ali Elkahky, Zhuoran Yu, Emily Pitler, Saran Lertpradit, Michael Mandl, Jesse Kirchner, Hector Fernandez Alcalde, Jana Strnadová, Esha Banerjee, Ruli Manurung, Antonio Stella, Atsuko Shimada, Sookyoung Kwak, Gustavo Mendonca, Tatiana Lando, Rattima Nitisaroj, and Josie Li. 2017 · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Meishan Zhang, Yue Zhang, and Guohong Fu. 2016a · 2016
Cited alongside, same era.
Fast and accurate neural word segmentation for chinese
Deng Cai, Hai Zhao, Zhisong Zhang, Yuan Xin, Yongjian Wu, and Feiyue Huang. 2017 · 2017
Cited alongside, same era.
Adversarial multi-criteria learning for chinese word segmentation
Xinchi Chen, Zhan Shi, Xipeng Qiu, and Xuanjing Huang. 2017 · 2017
Cited alongside, same era.
Transition-based neural word segmentation
Meishan Zhang, Yue Zhang, and Guohong Fu. 2016b
Cited in the paper.
Later among the works it cites.
Word-context character embeddings for chinese word segmentation
Hao Zhou, Zhenting Yu, Yue Zhang, Shujian Huang, XIN-YU DAI, and Jiajun Chen. 2017 · 2017
Later among the works it cites.
On the state of the art of evaluation in neural language models
Gábor Melis, Chris Dyer, and Phil Blunsom. 2018 · 2018
Closest in time.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Closest in time.