2024/11/06 by Aniket Deroy, Deroy, Aniket, Subhankar Maity +1 · 2 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Speech Recognition and Synthesis
paper · pdf · doi:10.48550/arxiv.2411.04025
openalex publication_date 2024/11/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Language Identification (LI) is crucial for various natural language processing tasks, serving as a foundational step in applications such as sentiment analysis, machine translation, and information retrieval. In multilingual societies like India, particularly among the youth engaging on social media, text often exhibits code-mixing, blending local languages with English at different linguistic levels. This phenomenon presents formidable challenges for LI systems, especially when languages intermingle within single words. Dravidian languages, prevalent in southern India, possess rich morphological structures yet suffer from under-representation in digital platforms, leading to the adoption of Roman or hybrid scripts for communication. This paper introduces a prompt based method for a shared task aimed at addressing word-level LI challenges in Dravidian languages. In this work, we leveraged GPT-3.5 Turbo to understand whether the large language models is able to correctly classify words into correct categories. Our findings show that the Kannada model consistently outperformed the Tamil model across most metrics, indicating a higher accuracy and reliability in identifying and categorizing Kannada language instances. In contrast, the Tamil model showed moderate performance, particularly needing improvement in precision and recall.