Exploring Marathi-English Code-Mixing: Comprehensive Analysis of NLP Applications (QA and NER)
Keywords:
Code-Mixed, Language Model, Marathi BERT, Natural Language Processing, Named Entity Recognition, Question AnsweringAbstract
Code-mixing, the linguistic practice of blending elements from multiple languages, is a common phenomenon that reflects the linguistic and cultural context of speakers. This research investigates Marathi-English code-mixing, with a focus on natural language processing (NLP) applications such as question answering (QA) and named entity recognition (NER). A sophisticated Marathi-English code-mixed QA system is proposed, which can comprehend and respond to questions that span multiple languages. The effectiveness of the system is evaluated using real and synthetic code-mixed QA datasets, revealing promising results, with the MuRIL model achieving an exact match (EM) score of 0.41 and 0.62 on real and synthetic datasets, respectively. The same model, when fine-tuned for code-mixed NER on the MahaRoBERTa code-mixed NER dataset, achieves an impressive F1 score of 73.92, outperforming other models in accurately labeling named entities in code-mixed text. This research advances code-mixed language processing by addressing issues in multilingual communication contexts.
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