Long Range Named Entity Recognition for Marathi Documents

TL;DR

This paper analyzes current NER techniques for Marathi, focusing on long-range entity recognition, compares models including BERT, and proposes adaptation strategies to improve Marathi NLP applications.

Contribution

It provides a comprehensive analysis of Marathi NER methods, evaluates BERT's potential for long-range recognition, and suggests adaptation strategies for Marathi language processing.

Findings

BERT shows promise for Marathi long-range NER

Comparison reveals strengths and weaknesses of existing methods

Proposed strategies improve Marathi NER performance

Abstract

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.