Multiclass Event Classification from Text
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10439967" target="_blank" >RIV/00216208:11320/21:10439967 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=3a6qL8FBp1" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=3a6qL8FBp1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1155/2021/6660651" target="_blank" >10.1155/2021/6660651</a>
Alternative languages
Result language
angličtina
Original language name
Multiclass Event Classification from Text
Original language description
Social media has become one of the most popular sources of information. People communicate with each other and share their ideas, commenting on global issues and events in a multilingual environment. While social media has been popular for several years, recently, it has given an exponential rise in online data volumes because of the increasing popularity of local languages on the web. This allows researchers of the NLP community to exploit the richness of different languages while overcoming the challenges posed by these languages. Urdu is also one of the most used local languages being used on social media. In this paper, we presented the first-ever event detection approach for Urdu language text. Multiclass event classification is performed by popular deep learning (DL) models, i.e.,Convolution Neural Network (CNN), Recurrence Neural Network (RNN), and Deep Neural Network (DNN). The one-hot-encoding, word embedding, and term-frequency inverse document frequency- (TF-IDF-) based feature vectors are used to evaluate the Deep Learning(DL) models. The dataset that is used for experimental work consists of more than 0.15 million (103965) labeled sentences. DNN classifier has achieved a promising accuracy of 84% in extracting and classifying the events in the Urdu language script.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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
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Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Scientific Programming
ISSN
1058-9244
e-ISSN
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Volume of the periodical
Neuveden
Issue of the periodical within the volume
13.01.2021
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
30
Pages from-to
6660651
UT code for WoS article
000613105800002
EID of the result in the Scopus database
2-s2.0-85099884066