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For many natural language processing (NLP) tasks the amount of annotated data is limited.
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In ICML-2001 . 282–289
John Lafferty, Andrew McСallum, and Fernando Pereira. 2001 · 2001
Earlier work this paper cites.
Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
Earlier work this paper cites.
A Survey on Metric Learning for Feature Vectors and Structured Data
Aurélien Bellet, Amaury Habrard, and Marc Sebban. 2013 · 2013
Earlier work this paper cites.
Towards robust linguistic analysis using ontonotes
Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Bjorkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong. 2013 · 2013
Earlier work this paper cites.
GloVe: Global Vectors for Word Representation. In Empirical Methods in Natural Language Processing (EMNLP) . 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Earlier work this paper cites.
Low-Resource Named Entity Recognition with Cross-lingual, Character-Level Neural Conditional Random Fields. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers) . Asian Federation of Natural Language Processing, 91–96
Ryan Cotterell and Kevin Duh. 2017 · 2016
Earlier work this paper cites.
Improved Named Entity Recognition using Machine Translation-based Cross-lingual Information
Sandipan Dandapat and Andy Way. 2016 · 2016
Earlier work this paper cites.
Label Embedding for Zero-shot Fine-grained Named Entity Typing. In International Conference on Computational Linguistics (COLING)
Yukun Ma, Erik Cambria, and Sa Gao. 2016 · 2016
Earlier work this paper cites.
One-shot Learning with Memory-Augmented Neural Networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy P. Lillicrap. 2016 · 2016
Cited alongside, same era.
Matching Networks for One Shot Learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, koray kavukcuoglu, and Daan Wierstra. 2016 · 2016
Cited alongside, same era.
SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data
Jason A. Fries, Sen Wu, Alexander Ratner, and Christopher Ré. 2017 · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning. In International Conference on Learning Representations (ICLR)
Sachin Ravi and Hugo Larochelle. 2017 · 2017
Cited alongside, same era.
Deep Active Learning for Named Entity Recognition. In Proceedings of the 2nd Workshop on Representation Learning for NLP . Association for Computational Linguistics, 252–256
Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue . Association for Computational Linguistics, 27–36
Tiancheng Zhao, Allen Lu, Kyusong Lee, and Maxine Eskenazi. 2017 · 2017
Later among the works it cites.
Sequence Labeling: A Practical Approach
Adnan Akhundov, Dietrich Trautmann, and Georg Groh. 2018 · 2018
Closest in time.
Learning How to Self-Learn: Enhancing Self-Training Using Neural Reinforcement Learning
Chenhua Chen and Yue Zhang. 2018 · 2018
Closest in time.
AllenNLP: A Deep Semantic Natural Language Processing Platform. In Proceedings of Workshop for NLP Open Source Software (NLP-OSS) . Association for Computational Linguistics, 1–6
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer. 2018 · 2018
Closest in time.
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Yanyao Shen, Hyokun Yun, Zachary Lipton, Yakov Kronrod, and Animashree Anandkumar. 2017 · 2017
Cited alongside, same era.
Prototypical Networks for Few-shot Learning
Jake Snell, Kevin Swersky, and Richard Zemel. 2017 · 2017
Cited alongside, same era.
Improving One-Shot Learning through Fusing Side Information
Yao-Hung Hubert Tsai and Ruslan Salakhutdinov. 2017 · 2017
Cited alongside, same era.
Deep contextualized word representations. In Proceedings of North American Chapter of the Association for Computational Linguistics
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018b
Cited in the paper.
Deep Contextualized Word Representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) . Association for Computational Linguistics, 2227–2237
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018a · 2018
Closest in time.
Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) . Association for Computational Linguistics, 1–15
Zhenghui Wang, Yanru Qu, Liheng Chen, Jian Shen, Weinan Zhang, Shaodian Zhang, Yimei Gao, Gen Gu, Ken Chen, and Yong Yu. 2018 · 2018
Closest in time.
Neural Cross-Lingual Named Entity Recognition with Minimal Resources. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, 369–379
Jiateng Xie, Zhilin Yang, Graham Neubig, Noah A. Smith, and Jaime Carbonell. 2018 · 2018
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