Comparative Study for Text Chunking Using Deep Learning: Case of Modern Standard Arabic

Authors

  • Nabil Khoufi University of Sfax
  • Chafik Aloulou University of Sfax

DOI:

https://doi.org/10.13053/cys-28-2-4560

Keywords:

NLP, Arabic language, Shallow parsing, Chunking, Deep learning, GRU, LSTM, BILSTM, ATB, Penn Arabic Treebank

Abstract

The task of chunking involves dividing a sentence into smaller phrases by identifying a limited amount of syntactic information. This process involves grouping together consecutive words to form phrases, also known as shallow parsing. Chunking does not provide information on the relationships between these phrases. This paper describes our approach to building chunking models for Arabic text using deep learning techniques. We evaluated several training models and compared their results using a rich data set. The results we obtained were highly encouraging when compared to previous related studies.

Author Biographies

Nabil Khoufi, University of Sfax

ANLP Research Group

Chafik Aloulou, University of Sfax

MIRACL Lab, Sfax

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Published

2024-06-12

Issue

Vol. 28 No. 2 (2024): 28(2) 2024

Section

Articles

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