Fetching the paper…
Reading the bibliography…
Character-level models of tokens have been shown to be effective at dealing with within-token noise and out-of-vocabulary words.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001 · 2001
Earlier work this paper cites.
Semi-markov conditional random fields for information extraction
Sunita Sarawagi and William W Cohen. 2005 · 2005
Earlier work this paper cites.
Joint word segmentation and POS tagging using a single perceptron
Yue Zhang and Stephen Clark. 2008 · 2008
Earlier work this paper cites.
A stacked sub-word model for joint chinese word segmentation and part-of-speech tagging
Weiwei Sun. 2011 · 2011
Earlier work this paper cites.
Incremental joint approach to word segmentation, pos tagging, and dependency parsing in chinese
Jun Hatori, Takuya Matsuzaki, Yusuke Miyao, and Jun’ichi Tsujii. 2012 · 2012
Earlier work this paper cites.
Extracting opinion expressions with semi-markov conditional random fields
Bishan Yang and Claire Cardie. 2012 · 2012
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.
On the properties of neural machine translation: Encoder–decoder approaches
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Learning character-level representations for part-of-speech tagging
Cícero N. dos Santos and Bianca Zadrozny. 2014 · 2014
Earlier work this paper cites.
Segmental recurrent neural networks
Lingpeng Kong, Chris Dyer, and Noah A Smith. 2015 · 2015
Earlier work this paper cites.
Robust morphological tagging with word representations
Thomas Müller and Hinrich Schütze. 2015 · 2015
Earlier work this paper cites.
Universal dependencies 1.2
Joakim Nivre et al. 2015 · 2015
Cited alongside, same era.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
Cited alongside, same era.
Neural word segmentation learning for chinese
Deng Cai and Hai Zhao. 2016 · 2016
Cited alongside, same era.
Multilingual language processing from bytes
Dan Gillick, Cliff Brunk, Oriol Vinyals, and Amarnag Subramanya. 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.
Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
Cited alongside, same era.
A semi-universal pipelined approach to the conll 2017 ud shared task
Hiroshi Kanayama, Masayasu Muraoka, and Katsumasa Yoshikawa. 2017 · 2017
Later among the works it cites.
Empower sequence labeling with task-aware neural language model
Liyuan Liu, Jingbo Shang, Frank Xu, Xiang Ren, Huan Gui, Jian Peng, and Jiawei Han. 2017 · 2017
Later among the works it cites.
Universal dependencies 2.0 CoNLL 2017 shared task development and test data. lindat/clarin digital library at the institute of formal and applied linguistics, charles university
Joakim Nivre and Lars Ahrenberg Željko Agic. 2017 · 2017
Later among the works it cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Achieving open vocabulary neural machine translation with hybrid word-character models
Minh-Thang Luong and Christopher D. Manning. 2016 · 2016
Cited alongside, same era.
End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
Xuezhe Ma and Eduard Hovy. 2016 · 2016
Cited alongside, same era.
Multilingual part-of-speech tagging with bidirectional long short-term memory models and auxiliary loss
Barbara Plank, Anders Søgaard, and Yoav Goldberg. 2016 · 2016
Cited alongside, same era.
Segment-level sequence modeling using gated recursive semi-markov conditional random fields
Jingwei Zhuo, Yong Cao, Jun Zhu, Bo Zhang, and Zaiqing Nie. 2016 · 2016
Cited alongside, same era.
IMS at the CoNLL 2017 UD shared task: CRFs and perceptrons meet neural networks
Anders Björkelund, Agnieszka Falenska, Xiang Yu, and Jonas Kuhn. 2017 · 2017
Cited alongside, same era.
A feature-enriched neural model for joint chinese word segmentation and part-of-speech tagging
Xinchi Chen, Xipeng Qiu, and Xuanjing Huang. 2017 · 2017
Cited alongside, same era.
Yuval Pinter, Robert Guthrie, and Jacob Eisenstein. 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.
Robsut wrod reocginiton via semi-character recurrent neural network
Keisuke Sakaguchi, Kevin Duh, Matt Post, and Benjamin Van Durme. 2017 · 2017
Later among the works it cites.
Character-based joint segmentation and pos tagging for chinese using bidirectional rnn-crf
Yan Shao, Christian Hardmeier, Jörg Tiedemann, and Joakim Nivre. 2017 · 2017
Later among the works it cites.
Tokenizing, pos tagging, lemmatizing and parsing UD 2.0 with UDPipe
Milan Straka and Jana Straková. 2017 · 2017
Later among the works it cites.
A general-purpose tagger with convolutional neural networks
Xiang Yu, Agnieszka Falenska, and Ngoc Thang Vu. 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, et al. 2017 · 2017
Later among the works it cites.
Wronging a right: Generating better errors to improve grammatical error detection
Sudhanshu Kasewa, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
Closest in time.
Hybrid semi-markov crf for neural sequence labeling
Zhi-Xiu Ye and Zhen-Hua Ling. 2018 · 2018
Closest in time.