Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Robotics and Control Systems
A. Alwahhab, Ahmed Bahaaulddin
Unknown Affiliation
Control & Systems Engineering Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : International Journal of Robotics and Control Systems

 1 
An Integrated Deep Learning Framework Combining LSTM-CRF, GRU-CRF, and CNN-CRF with Word Embedding Techniques for Arabic Named Entity Recognition Ali, Mahdi Ahmed; A. Alwahhab, Ahmed Bahaaulddin; Farjami, Yagoub
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1752

Abstract

Named entity recognition (NER) is the main function of natural language processing (NLP) and has many applications. Arabic NER systems aim to identify and classify Arabic NEs in Arabic text, which provide unique problems due to the language's complex morphology and syntactic structures. This paper provides an integrated deep learning system that incorporates three deep learning architectures—LSTM-CRF, GRU-CRF, and CNN-CRF—as well as three word embedding techniques: GloVe, Word2Vec, and FastText, all trained on Arabic corpus. To develop NER state-of-the-art in Arabic language, the present paper proposed a 3-stage process of pre-processing, feature extraction, and a combination of various deep network schemes. In the preprocessing section, operations such as removing irrelevant words, correcting words, etc. will be used to improve the system's efficiency. In the feature extraction section, three-word embedding methods, Glove, word2vec, and fasttext, which are trained with Arabic texts, are used, and finally, three LSTM-CRF, GRU-CRF, and CNN-CRF models are trained with each word embedding, and the results they are combined. Experimental results on benchmark dataset, ANERcorp show that our methodology is effective, with an accuracy of 94.39%, which outperforms other cutting-edge methods. However, combining multiple deep learning models with word embeddings increases computational complexity and resource requirements, potentially complicating implementation in resource-constrained contexts. Future efforts will concentrate on optimizing the framework to lower computational costs while keeping good performance.
Co-Authors Ali, Mahdi Ahmed Farjami, Yagoub