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Multiclass Event Classification from Text
- Source :
- Scientific Programming, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- Hindawi Limited, 2021.
-
Abstract
- 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.
- Subjects :
- Word embedding
Article Subject
Artificial neural network
Computer science
Event (computing)
business.industry
Feature vector
Deep learning
02 engineering and technology
computer.software_genre
Convolutional neural network
Computer Science Applications
QA76.75-76.765
020204 information systems
Classifier (linguistics)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer software
Artificial intelligence
business
tf–idf
computer
Software
Natural language processing
Subjects
Details
- ISSN :
- 1875919X and 10589244
- Volume :
- 2021
- Database :
- OpenAIRE
- Journal :
- Scientific Programming
- Accession number :
- edsair.doi.dedup.....fcfd25ffe767c668cb85d89d2542d8d8