COMPUTATIONAL CORPUS LINGUISTICS: A SYSTEMATIC LITERATURE REVIEW OF METHODOLOGICAL INTEGRATION, TECHNOLOGICAL INNOVATIONS, AND ARTIFICIAL INTELLIGENCE APPLICATIONS (2015-2025)
Keywords:
computational corpus linguistics, natural language processing, machine learning, artificial intelligence, deep learning, large language models.Abstract
This systematic literature review explores the convergence of computational methodologies and corpus linguistics between 2015 and 2025, synthesizing insights from 52 empirical studies retrieved from authoritative academic databases, including the ACL Anthology, Scopus, and Web of Science. The review examines how advances in natural language processing, machine learning, and artificial intelligence have reshaped the theoretical and methodological foundations of corpus linguistics, enabling analysis of massive textual datasets that exceed the capacity of traditional corpus tools. Findings reveal five dominant computational innovations driving this transformation: deep learning for automated annotation and classification, text similarity modeling for large-scale corpus comparison, topic modeling and distributional semantics for linguistic pattern discovery, neural machine translation for multilingual corpus processing, and large language model integration for corpus construction and analytical enhancement. These developments mark substantial progress in computational efficiency, scalability to billion-word corpora, and automation of formerly manual linguistic tasks. Nevertheless, the review identifies persistent challenges related to algorithmic bias, interpretability, ethical responsibility in automated language analysis, and the widening digital divide affecting under-resourced languages. Theoretically, the study maps emerging synergies between computational and linguistic paradigms, highlights hybrid research frameworks uniting symbolic and statistical approaches, and proposes ethical principles for responsible computational corpus inquiry. Practically, it underscores the urgency of interdisciplinary training that bridges linguistics and computer science, the development of interpretable and transparent machine learning models for linguistic research, and the equitable allocation of computational resources to support global linguistic diversity. The review concludes by outlining future research directions in explainable AI, multimodal corpus analysis, and decolonial perspectives on language technology development.
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