A unified survey that consolidates Indian NLP resources by task, language, domain, and modality while identifying gaps in coverage and generalization.
InFindings of the Association for Computational Linguistics: ACL 2023, pages 307–318
2 Pith papers cite this work. Polarity classification is still indexing.
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A language-adaptive combination of generalist, specialist, and ensemble transformer models achieves 0.796 macro F1 and 0.826 accuracy on multilingual polarization detection across 22 languages.
citing papers explorer
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BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources
A unified survey that consolidates Indian NLP resources by task, language, domain, and modality while identifying gaps in coverage and generalization.
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MKJ at SemEval-2026 Task 9: A Comparative Study of Generalist, Specialist, and Ensemble Strategies for Multilingual Polarization
A language-adaptive combination of generalist, specialist, and ensemble transformer models achieves 0.796 macro F1 and 0.826 accuracy on multilingual polarization detection across 22 languages.