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Neural Network Methods for Natural Language Processing
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
(2014) · Cited by 33,419
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,093
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
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Helmy, A. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.07588 · Cited by 2
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Bonilla, J. E., Hernández, L. M. M., & Larsson, M. M. (2025) · Cognitive Semantics · doi:10.1163/23526416-bja10079 · Cited by 1
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Metadata
- Authors
- Goldberg, Y.
- Affiliations
- Bar-Ilan University (IL)
- Corresponding author
- Goldberg, Yoav
- ORCID
- Goldberg, Yoav
- Venue
- Synthesis Lectures on Human Language Technologies
- Publisher
- Springer International Publishing
- Published
- 2017
- ISSN
- 1947-4040 (ISSN-L)
- 1947-4059
- OpenAlex
- W2606089314
- Semantic Scholar
- 6c27d9844fcb94dfcd6decab7df559134d6d4fd7
- Cited by
- 821
- References
- 75
- Citations by year
- 2026: 38 · 2025: 72 · 2024: 60 · 2023: 101 · 2022: 111 · 2021: 134 · 2020: 142 · 2019: 92 · 2018: 64 · 2017: 6
- FWCI
- 37.46
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Synthesis lectures on human language technologies (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- artificial neural networks, natural language processing, machine learning models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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