{'fraction_non_alphanumeric': 0.024640657084188913, 'fraction_numerical': 0.0, 'mean_word_length': 5.09375, '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}}
Named Entity Recognition using Hidden Markov Model (HMM)

Named Entity Recognition (NER)is the subtask of Natural Language Processing (NLP)which is the branch of artificial intelligence. It has many applications mainly inmachine translation, text to speech synthesis, natural language understanding, Information Extraction,Information retrieval, question answeringetc. The aim of NER is to classify words into some predefined categories like location name, person name, organization name, date, time etc. In this paper we describe the Hidden Markov Model (HMM) based approach of machine learning in detail to identify the named entities. The main idea behind the use of HMM model for buildingNER system is that it is language independent and we can apply this system for any language domain. In our NER system the states are not fixed means it is of dynamic in nature one can use it according to their interest. The corpus used by our NER system is also not domain specific.