Vol.35, No.1, 2023, pp.321-334, doi:10.32604/iasc.2023.026235
Language-Independent Text Tokenization Using Unsupervised Deep Learning
  • Hanan A. Hosni Mahmoud1, Alaaeldin M. Hafez2, Eatedal Alabdulkreem1,*
1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
* Corresponding Author: Eatedal Alabdulkreem. Email:
Received 19 December 2021; Accepted 24 February 2022; Issue published 06 June 2022
Languages–independent text tokenization can aid in classification of languages with few sources. There is a global research effort to generate text classification for any language. Human text classification is a slow procedure. Consequently, the text summary generation of different languages, using machine text classification, has been considered in recent years. There is no research on the machine text classification for many languages such as Czech, Rome, Urdu. This research proposes a cross-language text tokenization model using a Transformer technique. The proposed Transformer employs an encoder that has ten layers with self-attention encoding and a feedforward sublayer. This model improves the efficiency of text classification by providing a draft text classification for a number of documents. We also propose a novel Sub-Word tokenization model with frequent vocabulary usage in the documents. The Sub-Word Byte-Pair Tokenization technique (SBPT) utilizes the sharing of the vocabulary of one sentence with other sentences. The Sub-Word tokenization model enhances the performance of other Sub-Word tokenization models such pair encoding model by +10% using precision metric.
Text classification; language-independent tokenization; sub word tokenization
Cite This Article
H. A. Hosni Mahmoud, A. M. Hafez and E. Alabdulkreem, "Language-independent text tokenization using unsupervised deep learning," Intelligent Automation & Soft Computing, vol. 35, no.1, pp. 321–334, 2023.
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