A Review of the Marathi Natural Language Processing

TL;DR

This paper reviews the evolution of Marathi NLP, highlighting challenges like script diversity and resource scarcity, and discusses recent advances and available tools that have improved NLP research in Marathi and other Indian languages.

Contribution

It provides a comprehensive overview of Marathi NLP's development, current resources, tools, and techniques, emphasizing progress made over the past decade.

Findings

Significant growth in Marathi NLP resources and tools.

Challenges due to script diversity and morphological complexity.

Advances in neural models have improved NLP tasks in Marathi.

Abstract

Marathi is one of the most widely used languages in the world. One might expect that the latest advances in NLP research in languages like English reach such a large community. However, NLP advancements in English didn't immediately reach Indian languages like Marathi. There were several reasons for this. They included diversity of scripts used, lack of (publicly available) resources like tokenization strategies, high quality datasets \& benchmarks, and evaluation metrics. In addition to this, the morphologically rich nature of Marathi, made NLP tasks challenging. Advances in Neural Network (NN) based models and tools since the early 2000s helped improve this situation and make NLP research more accessible. In the past 10 years, significant efforts were made to improve language resources for all 22 scheduled languages of India. This paper presents a broad overview of evolution of NLP research in Indic languages with a focus on Marathi and state-of-the-art resources and tools available to the research community. It also provides an overview of tools \& techniques associated with Marathi NLP tasks.