NATURAL LANGUAGE PROCESSING PIPELINE FOR LOW RESOURCES INDIAN LANGUAGES

Research on the NLP pipeline in low-resource Indian languages highlights the importance of state-of-the-art computational methods for tackling language diversity and scarcity of data. And a few languages like Indian different variety of language having complex morphology, multiple scripts and ‘code mixing — has some unique challenges for NLP research. There are other domains like transfer learning and transformers which classical methods are not really well suited for the types of challenges this has been adopted (up to data till October 2023).This study demonstrates that use of multilingual modelling along with data augmentation and pre-processing methods in conjunction accommodates language-specific features thus improving the performance of models. Even on a small dataset, the results showed major gain over performance, speed and Scalability. The fact that the pipeline handles code-mixed language and different patterns of language makes it more applicable in real scenarios. Which makes the method useful for sectors like banking, education, government and online messaging that require processing local languages.             While these developments are undoubtedly impressive, the paper cites several issues: lack of large annotated data, lack of benchmarking resources and computational resources. We have many challenges and will need a collaborative approach among academics, industry professionals and policy makers. Standardized datasets and open-source software will be crucial in advancing much-needed NLP research on low-resource languages. In short, the NLP pipeline can be harnessed at scale for low-resource Indian languages. Correctional Enterprises aids digital inclusion by helping to close the digital divide. Future research needs to focus on the elements of model interpretability, debiasing, and language diversity so that all linguistic groups in India can benefit equally from Las in various applications.

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