Published Paper Details:

Part of Speech (POS) tagging refers to the process of classifying each morpheme, including punctuation marks, in a particular text document according to the context. An important first step in natural language processing is the parts of speech tagging (POS) process (NLP). To tag each word in the corpus with its appropriate parts of speech is its goal. Noun, pronoun, verb, adjective, and adverb are the fundamental POS tags, among others. POS tags are required for speech recognition and analysis, machine translation, lexical analysis such as word sense disambiguation, named entity recognition, information retrieval, and this system also assisted opinion mining by revealing the sentiments of a given text. POS taggers are also lacking in many Indian languages because basic resources like corpora and morphological analyzers are still being researched and developed. The following section of this research proposes a probabilistic Hidden Markov Model-based POS tagger for Gujarati. In order to reduce ambiguity and misclassification rates, the hidden Markov model predicts the hidden sequence based on the highest observation likelihood. A variety of POS tags at the word level make up the model that was tested using sample input text data.