Summary of Long Range Named Entity Recognition For Marathi Documents, by Pranita Deshmukh et al.
Long Range Named Entity Recognition for Marathi Documents
by Pranita Deshmukh, Nikita Kulkarni, Sanhita Kulkarni, Kareena Manghani, Geetanjali Kale, Raviraj Joshi
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of Natural Language Processing (NLP) methods, particularly Named Entity Recognition (NER), to manage long-range entities in Marathi-language digital content. It analyzes current NER techniques for Marathi documents and investigates the BERT transformer model’s potential for long-range Marathi NER. The study compares earlier methods and suggests adaptation strategies for Marathi literature, acknowledging the challenges posed by Marathi’s linguistic traits and contextual subtleties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to use computers to understand and identify important words in Indian stories written in a special language called Marathi. It wants to make it easier for computers to find things like people’s names and places in these stories, which is hard because Marathi is different from other languages. The researchers looked at what others have done to try to solve this problem and tried using a new way of understanding words that works well with English. |
Keywords
» Artificial intelligence » Bert » Named entity recognition » Natural language processing » Ner » Nlp » Transformer
