What is it about?
This research introduces the first Arabic dataset designed to help computers automatically find and recognize important technical terms in specialized texts. While English already has many resources for this task, Arabic has lacked such tools. To fill this gap, we created a carefully annotated dataset based on Arabic linguistics materials and used it to test modern AI language models. Our results show that Arabic AI models can learn to detect specialized terms with good accuracy, but they also face challenges due to the complexity of Arabic, such as word ambiguity and variations in expression. This work provides a foundation for improving Arabic language technologies, making it easier to build applications in areas like translation, search, and education.
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Why is it important?
Many modern technologies—like translation apps, search engines, and digital assistants—need to recognize important words and terms from different fields, such as science, law, or linguistics. For English, there are plenty of resources that help computers do this. But for Arabic, such resources were missing. This research fills that gap by creating the first dataset that teaches computers how to find important Arabic terms. This is important because it will: 1- Improve Arabic technology: make translation tools, search engines, and educational apps work better in Arabic. 2- Support research and learning: help students, teachers, and researchers access information more easily. 3- Preserve and promote Arabic: ensure that the Arabic language is included in the global development of AI and language technologies.
Perspectives
Working on this article was a meaningful experience because it addresses a real gap in Arabic language technology. For years, I have seen how Arabic lags behind English in AI research and applications, and I felt a responsibility to contribute to closing that gap. Creating the first annotated dataset for Arabic term extraction was both a challenge and a privilege, as it required bringing together linguistic knowledge and modern AI methods. My hope is that this work inspires more researchers to build on it, expand into new domains, and ensure that Arabic has a strong place in the future of artificial intelligence.
ABDULMOHSEN AL THUBAITY
HUMAIN
Read the Original
This page is a summary of: A Novel Dataset for Arabic Domain Specific Term Extraction and Comparative Evaluation of BERT-Based Models for Arabic Term Extraction, ACM Transactions on Asian and Low-Resource Language Information Processing, September 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3748323.
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