Deep Neural Network and Text Processing: A Literature Review

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Neural Network and Text Processing: A Literature Review

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Deep Neural Network and Text Processing: A Literature Review

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

139-144

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Chania

Datum konání akce

19. 7. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—