Long Range Named Entity Recognition for Marathi Documents
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909346731917312 |
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| author | Deshmukh, Pranita Kulkarni, Nikita Kulkarni, Sanhita Manghani, Kareena Kale, Geetanjali Joshi, Raviraj |
| author_facet | Deshmukh, Pranita Kulkarni, Nikita Kulkarni, Sanhita Manghani, Kareena Kale, Geetanjali Joshi, Raviraj |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09192 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Long Range Named Entity Recognition for Marathi Documents Deshmukh, Pranita Kulkarni, Nikita Kulkarni, Sanhita Manghani, Kareena Kale, Geetanjali Joshi, Raviraj Computation and Language Machine Learning 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. |
| title | Long Range Named Entity Recognition for Marathi Documents |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2410.09192 |