Combining rule-based and statistical mechanisms for low-resource named entity recognition

Author:

Gabbard RyanORCID,DeYoung Jay,Lignos ConstantineORCID,Freedman Marjorie,Weischedel Ralph

Funder

Defense Advanced Research Projects Agency

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics,Software

Reference17 articles.

1. Bonadiman D, Severyn A, Moschitti A (2015) Deep neural networks for named entity recognition in Italian. CLiC it 51–55

2. Collins M, Singer Y (1999) Unsupervised models for named entity classification. In: Proceedings of the joint SIGDAT conference on empirical methods in natural language processing and very large corpora, pp 100–110

3. Duchi JC, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159

4. Lafferty JD, McCallum A, Pereira FCN (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the eighteenth international conference on machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, ICML ’01, pp 282–289

5. Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Neural architectures for named entity recognition. CoRR abs/1603.01360, http://arxiv.org/abs/1603.01360

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