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Named Entity Recognition for Nepali Language

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Dec 2019Embargo end date: 01 Jan 2019Publisher:IEEEJournal:2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC)

Authors: Ankur Padia; Oyesh Mann Singh; Anupam Joshi;

doi: 10.1109/cic48465.2019.00031 , 10.48550/arxiv.1908.05828

arXiv: http://arxiv.org/abs/1908.05828

Named Entity Recognition for Nepali Language

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Abstract

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.

Related Organizations

University of Maryland, Baltimore
United States

Keywords

FOS: Computer and information sciences, Computer Science - Computation and Language, Computation and Language (cs.CL)

2 Research products, page 1 of 1

Nepali-News-Classifier software on GitHub
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NepaliStemmer software on GitHub
IsRelatedTo

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	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	8
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average