MAIN DIRECTIONS OF COMPUTATIONAL LINGUISTICS

Аннотация

Computational linguistics, an interdisciplinary field at the intersection of linguistics and computer science, focuses on developing algorithms and models to process and understand human language. This article explores the main directions of computational linguistics, highlighting its key areas of research and application. The article also examines emerging trends, such as the integration of deep learning in large language models and the ethical challenges of bias and inclusivity in language technologies. By analyzing these directions, the study underscores the transformative impact of computational linguistics on communication, artificial intelligence, and society. This overview provides a foundation for understanding the field’s theoretical advancements and practical implications, appealing to researchers, students, and professionals interested inthefuture oflanguage technologies. Keywords: Computational linguistics, Natural Language Processing (NLP), machine translation, speech recognition, speech synthesis, information retrieval, deep learning, Bias in NLP, Human- ComputerInteraction. Introduction: Computational linguistics, a dynamic field bridging linguistics and computer science, has become pivotal in shaping modern technologies that process and interpret human language. As artificial intelligence (AI) advances, the ability to model, analyze, and generate language has transformed applications ranging from virtual assistants to automated translation systems. This field addresses the complex challenge of enabling machines to understand and produce language in ways that mimic human capabilities, making it essential for innovations in communication, education, and information access. The significance of computational linguistics lies in its interdisciplinary nature, drawing on linguistic theory, statistical modeling, and machine learning to tackle real-world problems. However, the rapid evolution of language technologies raises questions about their theoretical foundations, practical limitations, and societal implications, necessitating a comprehensive exploration of the field’s core directions. Despite these advancements, gaps remain in understanding how emerging technologies, like large language models, integrate with traditional linguistic theories and address issues of accessibility across diverse languages. The literature also lacks a unified framework that synthesizes the field’s diverse directions for both academic and practical audiences.This article investigates the primary directions of computational linguistics, aiming to address the question: What are the core areas driving the field’s development, and how do they shape its future trajectory? The objective is to provide a clear, accessible overview of these directions, highlighting their theoretical underpinnings, practical applications, and challenges. By doing so, this work seeks to inform researchers, students, and practitioners about the evolving landscape of computational https://ijmri.de/index.php/jmsi volume 4,issue3,2025 774 linguistics and its rolein advancing AI-driven language solutions. Methods: To investigate the main directions of computational linguistics, this study employed a qualitative research design focused ona systematic literature review and case study analysis. The approach was selected to synthesize existing knowledge and examine practical applications of computational linguistics, allowing for a comprehensive exploration of its core areas. The research design combined descriptive and analytical methods to map the field’s theoretical foundations and technological advancements. This study did not involve experimental manipulation or primary data collection from human participants but relied on secondary data sources, including academic publications, technical reports, and open-access datasets. The methodology was structured to ensure replicability, with clearly defined steps for data collection andanalysis. Data Collection: Data were collected from multiple secondary sources to ensure a robust representation of computational linguistics research. For the literature review, academic publications were sourced from databases such as Google Scholar, IEEE Xplore, and ACL Anthology, covering peer-reviewed journal articles, conference proceedings, and book chapters published between 2015 and 2025. Search terms included “computational linguistics,” “natural language processing,” “machine translation,” “speech recognition,” “speech synthesis,” “information retrieval,” and “large language models.” Inclusion criteria required publications to focus on theoretical frameworks, methodologies, or applications within computational linguistics, with preference given to works in English. Technical documentation, white papers, and open- source repositories (e.g., GitHub) provided detailed information on these applications’ architectures, training datasets, and evaluation metrics. Additionally, publicly available datasets, such as the Comm