Exploring Marathi Language Processing

Exploring the Marathi Language

Marathi, a language rooted in ancient India, is spoken by over 83 million people, primarily in the western state of Maharashtra. It's the third most widely spoken language in India and the 15th most spoken worldwide. Marathi has a rich literary history, a complex script, and a diverse array of dialects.

Grammar and Structure

Marathi follows a Subject-Object-Verb (SOV) structure, with nouns inflecting for gender, number, and case. The language has eight main parts of speech: noun, verb, adjective, adverb, pronoun, postposition, conjunction, and interjection.

Marathi dialects include Varhadii, Gawdi, Nagpuri Marathi, Dangii, Malwani, Kudali, Kasargod, Kosti, Ahirani, and others.

Resources and Challenges

Compared to other languages, Marathi has fewer resources and tools available for Natural Language Processing (NLP). The largest publicly-available corpora for Indian languages, such as IndicCorp, includes Marathi, consisting of over 111 thousand web sources.

Marathi WordNet, created by Dr. Pushpak Bhattacharya, is a machine-readable dictionary that uses paradigmatic relations such as synonymy, hyponymy, antonymy, and entailment to construct a semantic network.

Two libraries, Indic NLP and Natural Language Toolkit for Indic Languages (iNLTK), support Marathi and other Indian languages for common text processing and NLP tasks.

Speech Recognition and Sentiment Analysis

Marathi speech recognition research is still in its early stage, with limited work being done to develop speech-based systems. In the realm of sentiment analysis, MahaEmoSen, an emotion-aware multimodal approach, uses deep neural networks to analyze Marathi sentiment in tweets.

Language Processing Resources and Techniques

A survey on NLP resources, tools, and techniques for Marathi language processing provides insight into the various linguistic resources and characteristics of Marathi. The study highlights the availability of corpus, tools, and techniques to be used to develop NLP tasks for Marathi.

Researchers also address the challenges of resolving ambiguity in Marathi language text through rule-based solutions and machine learning techniques.

Conclusion

The Marathi language has made significant progress in NLP research, despite being one of the Indian languages with fewer resources and tools available. Researchers are working to develop resources and techniques to improve Marathi language processing, including speech recognition, sentiment analysis, and Word Sense Disambiguation.

As Marathi continues to grow in popularity and relevance, the development of new resources, tools, and techniques will be essential to advancing Marathi language processing and ensuring that the language remains a vibrant and dynamic part of the digital landscape.