Multi-lingual language variety identification using conventional deep learning and transfer learning approaches
Joint Authors
Hamid, Samih Nurin
Ashraf, Muhammad Adnan
Ya Nan, Qiao
Source
The International Arab Journal of Information Technology
Issue
Vol. 19, Issue 5 (30 Sep. 2022), pp.705-712, 8 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2022-09-30
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
Language variety identification tends to identify lexical and semantic variations in different varieties of a single language.
Language variety identification helps build the linguistic profile of an author from written text which can be used for cyber forensics and marketing purposes.
Investigating previous efforts for language variety identification, we hardly find any study that experiments with transfer learning approaches and/or performs a thorough comparison of different deep learning approaches on a range of benchmark datasets.
So, to bridge this gap, we propose transfer learning approaches for language variety identification tasks and perform an extensive comparison of them with deep learning approaches on multiple varieties of four widely spoken languages, i.e., Arabic, English, Portuguese, and Spanish.
This research has treated this task as a binary classification problem (Portuguese) and multi-class classification problem (Arabic, English, and Spanish).
We applied two transfer learning Bidirectional Encoder Representations from Transformers (BERT), Universal Language Model Fine-tuning (ULMFiT), three deep learning-Convolutional Neural Networks (CNN), Bidirectional Long Short Term Memory (Bi-LSTM), Gated Recurrent Units (GRU), and an ensemble approach for identifying different varieties.
A thorough comparison between the approaches suggests that the transfer learning based ULMFiT model outperforms all other approaches and produces the best accuracy results for binary and multi-class language variety identification tasks.
American Psychological Association (APA)
Hamid, Samih Nurin& Ashraf, Muhammad Adnan& Ya Nan, Qiao. 2022. Multi-lingual language variety identification using conventional deep learning and transfer learning approaches. The International Arab Journal of Information Technology،Vol. 19, no. 5, pp.705-712.
https://search.emarefa.net/detail/BIM-1437072
Modern Language Association (MLA)
Hamid, Samih Nurin…[et al.]. Multi-lingual language variety identification using conventional deep learning and transfer learning approaches. The International Arab Journal of Information Technology Vol. 19, no. 5 (Sep. 2022), pp.705-712.
https://search.emarefa.net/detail/BIM-1437072
American Medical Association (AMA)
Hamid, Samih Nurin& Ashraf, Muhammad Adnan& Ya Nan, Qiao. Multi-lingual language variety identification using conventional deep learning and transfer learning approaches. The International Arab Journal of Information Technology. 2022. Vol. 19, no. 5, pp.705-712.
https://search.emarefa.net/detail/BIM-1437072
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 710-712
Record ID
BIM-1437072