Nisheeth Joshi

Papers

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About

Nisheeth Joshi is a prominent researcher in Natural Language Processing (NLP) and Artificial Intelligence, with a particular focus on under-resourced Indian languages. His work centers on advancing Named Entity Recognition (NER)—a critical NLP task that identifies and classifies named entities such as persons, organizations, and locations within text. Joshi’s major contribution lies in surveying and evaluating diverse machine learning and deep learning approaches for NER tailored to the linguistic complexities of Indian languages, which often lack extensive annotated datasets. His highly cited survey paper, “A Survey on Various Approaches Used in Named Entity Recognition for Indian Languages” (2022), has garnered 4 citations, reflecting its foundational role in guiding subsequent research. Beyond this, Joshi has explored machine translation, sentiment analysis, and language resource development, consistently bridging computational methods with real-world linguistic challenges. His work is notable for its practical impact, aiding the development of tools for information extraction, digital humanities, and multilingual AI systems. Joshi’s research continues to inspire students and researchers working on low-resource language technologies, making him a key figure in the advancement of inclusive NLP.