What is it about?
For the first time, this paper has explored tokenization and stemming in the Limbu language, a low-resource language with complex morphology. Tokenization involves breaking text into meaningful units (such as words or phrases), while stemming reduces words to their root forms to aid text processing. Given Limbu’s unique script and rich inflections, standard techniques had to be adapted. This study marks an important step toward developing natural language processing (NLP) tools for Limbu, enabling better text analysis, search, and machine translation.
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Why is it important?
Tokenization and stemming are crucial for developing Natural Language Processing (NLP) tools for the Limbu language, which lacks digital resources due to its complex morphology and unique script. This research enhances text processing, improving applications like search engines, spell checkers, and machine translation. It also plays a vital role in preserving and modernizing Limbu, facilitating linguistic research, and bridging the digital divide by enabling AI-driven tools for its speakers.
Perspectives
This pioneering work on tokenization and stemming in Limbu opens several avenues for future research and development. In the short term, refining these techniques can enhance text processing accuracy, aiding applications like machine translation, text summarization, and search engines. In the long run, integrating these methods into deep learning models could enable speech recognition, sentiment analysis, and AI-driven linguistic tools for Limbu. Additionally, this research can contribute to the digital preservation of Limbu, encouraging governmental and academic efforts to develop NLP tools for other underrepresented languages. Collaborations with linguists, computational researchers, and native speakers will be essential in expanding this work, ensuring that Limbu remains relevant in the digital age.
Tokenization and Stemming of Limbu language Abigail Rai
Sikkim Manipal Institute of Technology (SMIT)
Read the Original
This page is a summary of: Tokenization and Stemming of Limbu Language, ACM Transactions on Asian and Low-Resource Language Information Processing, January 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3712018.
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