A Named Entity Recognition System for the Marathi Language

Named entity recognition is a complex task in developing many NLP applications. This is one of the essential requirements of language modeling in NLP; without it, it is not possible to proceed further and achieve better results. In this proposed task, we have designed a hybrid technique that is a combination of machine learning and a rule-based approach. This system is to identify such named entities that belong under a specific class, creating a special identification and importance in the meaning generation as well as understanding of the language. This is concerned with the input text. Named entity recognition is important for different group items, such as a person’s name, location or place, animals, organization, time or date, etc. Named entities are informative and good representatives of knowledge. NE also explores the knowledge of artificial intelligence-based systems or expert systems. Using the proposed hybrid model, we have achieved 59.40% performance in identifying named entities and properly labeling for the Marathi