L3Cube-MahaPOS: A Marathi Part-of-Speech Tagging Dataset and BERT Models
Part-of-Speech (POS) tagging is a foundational NLP task underpinning machine translation, information extraction, and syntactic parsing. Despite Marathi being spoken by over 83 million people and ranking among the top twenty most spoken languages worldwide, it remains severely under-resourced in annotated corpora and standardised evaluation benchmarks. Marathi presents unique challenges for computational modelling owing to its rich morphology, relatively free word order, lack of capitalisation conventions, and pervasive code-mixing with Hindi and English. We introduce L3Cube-MahaPOS, a gold-standard POS tagging dataset for Marathi comprising 32,354 manually annotated sentences drawn from ...
| Authors | Hariom Ingle, Ronit Ghode, Ishwari Gondkar et al. |
|---|---|
| Primary category | cs.CL |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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