Natural Language Processing in Low-Resource Language Contexts

Abstract

Natural Language Processing (NLP) in low- resource language surrounds present unique challenges due to limited data vacuity and verbal diversity. This research explores innovative methodologies to address these challenges, using data addition for robust preprocessing, Bag- of- Words for effective point selection, and advanced bracket ways like intermittent Neural Networks (RNNs). Data addition ways, including reverse relief and back- restatement, are employed to expand the dataset, perfecting the representation of under- resourced languages. The arc approach captures essential verbal patterns, serving as a foundational point selection system for successional data processing. RNNs is employed to classify textbook by using their capacity to model long- term dependences in language, icing effective literacy of syntactic and semantic nuances. This integrated approach demonstrates bettered delicacy and rigidity, offering a scalable frame for advancing NLP operations in low- resource language surrounds while fostering inclusivity in global computational linguistics.