A Review of the Marathi Natural Language Processing
It provides a comprehensive overview for researchers working on low-resource languages, but it is incremental as it synthesizes existing developments without introducing new methods.
This paper reviews the evolution of natural language processing (NLP) research for Indic languages, focusing on Marathi, and outlines the state-of-the-art resources and tools available, addressing challenges like script diversity and lack of datasets.
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.