Machine Learning Techniques for Effective Multilingual Text Classification

Abstract

The rapid growth of the internet and digital communication has led to an unprecedented increase in textual content across numerous languages worldwide. As a result, the field of Natural Language Processing (NLP) faces a critical challenge in accurately identifying and recognizing languages, especially in multilingual environments. This study highlights the critical role of multilingual identification and recognition systems in enabling communication across diverse linguistic environments. These systems serve as foundational tools for a variety of applications, such as translation services and speech recognition, which rely on accurately identifying and understanding languages spoken or written in various contexts. This paper presents a systematic evaluation of such systems, focusing specifically on English, Hindi and Marathi languages. A range of approaches, including machine learning models, deep learning, and NLP, were analyzed. The inclusion criteria for this study consisted of research publications from the years 2019-2024. Overall, the findings underscore the importance of identification of multilingual languages. This review offers substantial insights for both researchers and practitioners engaged in the advancement of robust multilingual identification and recognition systems designed for English, Hindi and Marathi languages. Further study is imperative to overcome current obstacles and augment the efficiency of these multilingual identification and recognition techniques.