The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
A Primer on Neural Network Models for Natural Language Processing
Open accessAbstract
Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This tutorial surveys neural network models from the perspective of natural language processing research, in an attempt to bring natural-language researchers up to speed with the neural techniques. The tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.
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Cites
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Distributed Representations of Words and Phrases and their Compositionality
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Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921
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Metadata
- Authors
- Goldberg, Y.
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 2016-11-20
- Volume
- 57
- Pages
- 345-420
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W1888026739
- Cited by
- 1,201
- References
- 11
- FWCI
- 118.95
- Type
- journal article
- Language
- en
- Open access
- diamond
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