Advances in Computational Linguistics through Big Data: Deep Learning Approaches to Natural Language Processing

Abstract

Computational linguistics has entered a transformative era with the integration of Big Data and deep learning. Traditional approaches to natural language processing (NLP) relied on rule-based systems and limited corpora, often constrained by linguistic coverage and scalability. The advent of Big Data has made it possible to train large-scale neural architectures capable of modeling complex linguistic phenomena across diverse languages and domains. This article examines how Big Data-driven deep learning advances computational linguistics in three key areas: semantic representation, language generation, and cross-linguistic modeling. Using data from large-scale repositories, including multilingual web corpora and open-source datasets, we demonstrate how deep neural networks outperform traditional models in both accuracy and adaptability. The results highlight not only technical progress but also challenges related to interpretability, bias, and ethical implications. We argue that computational linguistics, strengthened by Big Data, is moving beyond descriptive modeling to predictive and generative capabilities that reshape communication technologies, education, and cross-cultural understanding.