{'fraction_non_alphanumeric': 0.0150093808630394, 'fraction_numerical': 0.0, 'mean_word_length': 4.933333333333334, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Hidden Markov model for POS tagging in word sense disambiguation

The process of identifying the sense or meaning of a word where the word has multiple meanings in a sentence is word sense disambiguation. Parts-of-speech tagging refers to a process of assigning a grammatical category like noun, adjective, verb etc. to a word to study the lexical information and context which is used for word sense disambiguation. Calculating Emission and Transition probability using Hidden Markov model is a commendable approach for POS tagging.