Work place: SVKM’s Institute of Technology, Dhule, Maharashtra 424001, India
E-mail: meetpatilkavita@gmail.com
Website:
Research Interests: Artificial Intelligence
Biography
DR. Kavita Tukaram Patil working as an Assistant Professor in the Department of Computer Engineering at SVKM’s Institute of Technology, Dhule, Maharashtra. She is graduated in Computer Engineering at D. N. Patel College of Engineering, Shahada, Nandurbar, Maharashtra, India. She secured Master of Computer Science and Engineering at SSBT’s COET, Bambhori, Jalgaon, Maharashtra, India. She secured Ph.D. in Computer Engineering at School of Computer Sciences, KBCNMU, Jalgaon, Maharashtra, India. Apart from academic qualification she also qualified UGC-NET-2023 in computer science and application. She has organized 13 FDP and attended more than 57 FDPs. She has delivered two expert talk on “Project Guidelines and Research Paper Writing”. She works as RPS committee member for Ph.D. Candidates at Sandip University Nashik. She is in teaching profession for more than 13 years. She has presented and published 18 papers in National and International Journals, Conference and Symposiums. She also published one copyright, two patent, one book, one scopus indexed book chapter. She is honored with “Research Excellence Award-2024” by Institute of Scholars. Her main area of interest includes MANET, Artificial Intelligence, Natural Language Processing, Machine Learning, and Deep Learning.
By Swati Prakash Sonawane Kavita Tukaram Patil R. P. Bhavsar B. V. Pawar
DOI: https://doi.org/10.5815/ijitcs.2026.03.02, Pub. Date: 8 Jun. 2026
Part-of-Speech (POS) tagging is an essential and important pre-processing activity for many Natural Language Processing (NLP) applications, this is particularly more evident for morphologically rich languages such as Marathi. This research investigates POS tagging for Marathi using the Maximum Entropy Markov Model (MEMM). MEMM combines the strengths of conditional probability modelling and sequence prediction, allowing the integration of rich contextual features. Features used include word forms, suffixes, prefixes, and neighboring tags, effectively tackling the challenges presented by inflectional variations and ambiguity in Marathi. Experimental results demonstrate that the MEMM-based POS tagger achieves an accuracy of 83.72%. This performance marks a notable advancement in Marathi POS tagging, given the linguistic diversity and the scarcity of annotated data. Error analysis enhances the issues like ambiguity in homonyms and out-of-vocabulary words, providing methods for further improvement through enriched datasets and sophisticated modelling techniques. This study enhances NLP applications such as machine translation, spell checking, and sentiment analysis for Indian languages and offers a solid foundation for future research in Marathi POS tagging.
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