Named Entity Recognition for Nepali Language
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Abstract (arXiv)
Named Entity Recognition have been studied for different languages like English, German, Spanish and many others but no study have focused on Nepali language. In this paper we propose a neural based Nepali NER using latest state-of-the-art architecture based on grapheme-level which doesn't require any hand-crafted features and no data pre-processing. Our novel neural based model gained relative improvement of 33% to 50% compared to feature based SVM model and up to 10% improvement over state-of-the-art neural based model developed for languages beside Nepali.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Aug 2019.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.
3 papers.
- 2022MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognitioncited by 38
- 2024Can Perplexity Predict Fine-tuning Performance? An Investigation of Tokenization Effects on Sequential Language Models for Nepalicited by 3
- 2025Generative AI for Named Entity Recognition in Low-Resource Language Nepalicited by 0
- 2025Generative AI for Named Entity Recognition in Low-Resource Language Nepali12 Mar 2025
- 2024Can Perplexity Predict Fine-tuning Performance? An Investigation of Tokenization Effects on Sequential Language Models for Nepali28 Apr 2024
- 2022MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition22 Oct 2022