ArticleData in brief2025
PROSHNO BINNASH: Contextual multi-label question answering dataset for low-resource NLP.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
4 authors.
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Abstract
Responding to the demand for automated learning tools in the Bangla language, this work introduces PROSHNO BINNASH, a unique contextual multi-label question answering dataset designed for low-resource natural language processing (NLP) tasks. The dataset comprises 4069 carefully curated entries sourced from true Bengali texts, along with query banks, literature, and historical narratives. Every entry carries a context, a question, its corresponding answer, and a set of multi-label annotations across ten categories: Sports, Health & Exercise, Literature, Prominent Person, Movie, Science, Liberation War, Politics, Artist, and History. PROSHNO BINNASh is entirely human-generated, without relying on machine-generated or translated content. The type schema permits one-to-many relationships among questions and classes, taking into account the nuanced expertise of educational content material. Contexts, questions, and answers were annotated and demonstrated with the useful resource of location experts to ensure accuracy and relevance. This data set is particularly useful for education and Bangla analytics, multi-label class structures, and academic chatbot comparisons. Via contributing a wealthy, native-language, useful resource, PROSHNO BINNASH aims to fill an essential hole in Bangla NLP research and help the advancement of inclusive, AI-pushed educational technology for hundreds of thousands of Bengali speakers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.