Visualizing Parts of Speech Tags by Analysing English Language Text

Part of Speech (POS) tagging is vital in natural language processing (NLP) for various applications. Hidden Markov Models (HMMs) have traditionally been used for this task, yet they struggle with capturing complex dependencies. This study explores Conditional Random Fields (CRFs) and their integration with Spacy for POS tagging. CRFs offer better sequence modelling capabilities, while Spacy provides rich linguistic features. Experimental evaluation demonstrates superiority of CRFs and CRF with Spacy over HMMs. They exhibit improved accuracy, particularly in capturing context and handling ambiguous sequence. The findings underscore the efficiency of advanced techniques like CRFs and their integration with comprehensive NLP libraries like Spacy for accurate POS tagging. Such methods are crucial for advancing NLP applications.