# Long Range Named Entity Recognition for Marathi Documents

arXiv: https://arxiv.org/abs/2410.09192 · HTML: https://mlsanity.com/paper/2410.09192/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources](https://mlsanity.com/paper/2202.01159/) (2022)
- [L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library](https://mlsanity.com/paper/2205.14728/) (2022)
- [Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages](https://mlsanity.com/paper/2212.10168/) (2022)
- [Mono vs Multilingual BERT: A Case Study in Hindi and Marathi Named Entity Recognition](https://mlsanity.com/paper/2203.12907/) (2022)
- [Improving Multi-Word Entity Recognition for Biomedical Texts](https://mlsanity.com/paper/1908.05691/) (2019)

## Built on by (most cited first, 1 of 1)

- [Named Entity Recognition using Sliding Window Approach](https://mlsanity.com/paper/2609.29682/) (2026)

## Abstract (arXiv)

The demand for sophisticated natural language processing (NLP) methods, particularly Named Entity Recognition (NER), has increased due to the exponential growth of Marathi-language digital content. In particular, NER is essential for recognizing distant entities and for arranging and understanding unstructured Marathi text data. With an emphasis on managing long-range entities, this paper offers a comprehensive analysis of current NER techniques designed for Marathi documents. It dives into current practices and investigates the BERT transformer model's potential for long-range Marathi NER. Along with analyzing the effectiveness of earlier methods, the report draws comparisons between NER in English literature and suggests adaptation strategies for Marathi literature. The paper discusses the difficulties caused by Marathi's particular linguistic traits and contextual subtleties while acknowledging NER's critical role in NLP. To conclude, this project is a major step forward in improving Marathi NER techniques, with potential wider applications across a range of NLP tasks and domains.