VITERBI BASED PARTS OF SPEECH TAGGING FOR HINDI AND MARATHI

Machine translation has expanded immensely, particularly in this period. Machine translation can be broken into seven main steps namely- token generation, analyzing morphology, lexeme, tagging Part of Speech, chunking, parsing, and disambiguation in words. NLP is a promising field of research, which enables the machine to analyze and process the meaning behind human languages. The aim of our project is to assign a specific grammatical class to the input sequence of Hindi and Marathi language. Major part of India's population belongs to rural areas and these people are more comfortable and well acquainted with Hindi and Marathi Language. It is considered one of the official languages of India. But, as most of the material available online today is in English it becomes difficult for them to understand it. So, to ease up their interaction with the online portal and to make it effective, language translation comes into view and Natural Language Processing plays a key role in it. From speech recognition to sentiment analysis, NLP is the backbone of this interaction. Furthermore, for development of any NLP application, POS tagging is a necessary step. English language tagging is already available so our concentration was basically more on Hindi and Marathi corpus POS tagging. Although there are many approaches available for POS tagging like rule- based POS tagging, lexical analysis etc. we have considered the stochastic based POS tagging for our project because of its better results in other languages.