What is it about?
The paper addresses the challenges of POS tagging in the Limbu language—a low-resource language with limited annotated data. Employing deep learning techniques, specifically a Bi-LSTM-CRF model enhanced through transfer learning and multilingual tutoring, the researchers developed a POS tagging system that achieved a 90% accuracy rate.
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Why is it important?
Preservation of Low-Resource Languages. Advancement in NLP for Underrepresented Languages Scalable Methodology. Enabling Further Linguistic Research. Cultural and Educational Value.
Perspectives
This study highlights the potential of deep learning for low-resource languages like Limbu, offering a scalable approach for NLP tasks. It supports language preservation, enables educational tools, and lays a foundation for future research in linguistic and technological development.
Tokenization and Stemming of Limbu language Abigail Rai
Sikkim Manipal Institute of Technology (SMIT)
Read the Original
This page is a summary of: Part-of-Speech (POS) Tagging of Low-Resource Language (Limbu) with Deep learning, Panamerican Mathematical Journal, November 2024, Science Research Society,
DOI: 10.52783/pmj.v35.i1s.2297.
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