CovTiNet: Covid text identification network using attention-based positional embedding feature fusion

Md Rajib Hossain, Mohammed Moshiul Hoque, Nazmul Siddique, Iqbal H Sarker

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
19 Downloads (Pure)

Abstract

Covid text identification (CTI) is a crucial research concern in natural language processing (NLP). Social and electronic media are simultaneously adding a large volume of Covid-affiliated text on the World Wide Web due to the effortless access to the Internet, electronic gadgets and the Covid outbreak. Most of these texts are uninformative and contain misinformation, disinformation and malinformation that create an infodemic. Thus, Covid text identification is essential for controlling societal distrust and panic. Though very little Covid-related research (such as Covid disinformation, misinformation and fake news) has been reported in high-resource languages (e.g. English), CTI in low-resource languages (like Bengali) is in the preliminary stage to date. However, automatic CTI in Bengali text is challenging due to the deficit of benchmark corpora, complex linguistic constructs, immense verb inflexions and scarcity of NLP tools. On the other hand, the manual processing of Bengali Covid texts is arduous and costly due to their messy or unstructured forms. This research proposes a deep learning-based network (CovTiNet) to identify Covid text in Bengali. The CovTiNet incorporates an attention-based position embedding feature fusion for text-to-feature representation and attention-based CNN for Covid text identification. Experimental results show that the proposed CovTiNet achieved the highest accuracy of 96.61±.001% on the developed dataset (BCovC) compared to the other methods and baselines (i.e. BERT-M, IndicBERT, ELECTRA-Bengali, DistilBERT-M, BiLSTM, DCNN, CNN, LSTM, VDCNN and ACNN).
Original languageEnglish
Pages (from-to)13503-13527
Number of pages25
JournalNeural Computing and Applications
Volume35
Issue number18
Early online date14 Mar 2023
DOIs
Publication statusPublished (in print/issue) - 30 Jun 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Keywords

  • Natural Language Processing
  • Transformers
  • Deep Learning
  • Self-attention
  • Low-Resource Languages
  • Positional Encoding
  • Covid Text Identification
  • Embedding Feature Fusion
  • Covid text identification
  • Positional encoding
  • Low-resource languages
  • Deep learning
  • Natural language processing
  • Embedding feature fusion

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