Deep Neural Network and Text Processing: A Literature Review
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10252035" target="_blank" >RIV/61989100:27240/22:10252035 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/abstract/document/10017923" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10017923</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CSCC55931.2022.00033" target="_blank" >10.1109/CSCC55931.2022.00033</a>
Alternative languages
Result language
angličtina
Original language name
Deep Neural Network and Text Processing: A Literature Review
Original language description
Deep learning is a powerful representation training algorithm which has been used to understand context clues. This paper has provided review of past research on neural networks in their use of in text analysis. Neural networks were observed to use a number of computational layers to understand hierarchical representations of the data, resulting in cutting edge results in a range of domains. This article carried out an empirical assessment of vital deep learning related techniques and frameworks to investigate their use in varied NLP tasks, as well as contextualizing, making comparisons, and comparing the various models and gives a clear knowledge of the relevant facets of deep neural network use in NLP. (C) 2022 IEEE.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
26th International Conference on Circuits, Systems, Communications and Computers, CSCC 2022 : proceedings : 19-22 July 2022, Chania, Crete Island, Greece
ISBN
978-1-66548-187-8
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
139-144
Publisher name
IEEE
Place of publication
Piscataway
Event location
Chania
Event date
Jul 19, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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