Linguistic modeling in computational linguistics: Modern approaches to building algorithms and developing machine learning systems

Abstract

This study examines computational linguistics as an interdisciplinary field that integrates linguistics and computer science, with a particular focus on language modeling as a fundamental component of natural language processing and artificial intelligence systems. The research aims to analyze the challenges facing language modeling, including linguistic challenges related to the complexity and multi-level structure of language, as well as technical challenges associated with data availability, quality, computational resources, and algorithm design.

      The study also explores the evolution of language models, from traditional statistical models to neural approaches and large-scale language models based on deep learning and transformer architectures. Furthermore, it investigates different approaches to algorithm development, comparing rule-based methods with data-driven techniques, and highlighting the role of big data and pretraining in enhancing model performance.

     The study concludes that, despite significant advancements, language modeling still faces complex challenges—particularly in the context of the Arabic language—thus requiring integrated approaches that combine linguistic insights with advanced computational techniques to develop more accurate and efficient natural language processing systems.