Abstract

Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution. We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.

Keywords

Computer scienceArtificial intelligenceDeep learningContext (archaeology)Variety (cybernetics)Natural language processingMachine learning

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Publication Info

Year
2018
Type
article
Volume
13
Issue
3
Pages
55-75
Citations
2738
Access
Closed

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Cite This

Tom Young, Devamanyu Hazarika, Soujanya Poria et al. (2018). Recent Trends in Deep Learning Based Natural Language Processing [Review Article]. IEEE Computational Intelligence Magazine , 13 (3) , 55-75. https://doi.org/10.1109/mci.2018.2840738

Identifiers

DOI
10.1109/mci.2018.2840738
arXiv
1708.02709

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Data completeness: 79%