Long Range Named Entity Recognition for Marathi Documents
It addresses the need for improved NER in Marathi to organize and understand unstructured text data, which is incremental as it builds on existing techniques for a specific language.
This paper analyzes existing Named Entity Recognition (NER) methods for Marathi documents, focusing on handling long-range entities, and explores the potential of BERT transformer models for this task, comparing with English NER and suggesting adaptations.
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.