{'fraction_non_alphanumeric': 0.013388259526261586, 'fraction_numerical': 0.0, 'mean_word_length': 5.151898734177215, '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}}
The Application of Hidden Markov Model Based on Semantic Case Amelioration in Chinese Word Sense Tagging

Word sense tagging is one of the difficult points in the field of natural language processing. This paper has studied Chinese word sense tagging with the hidden Markov model (HMM) based on semantic case amelioration in order to make use of statistical methods. Firstly,word sense tagging to the real text for application was carried on the HowNet, which is a kind of repository and regards the concept, which represented by words and expressions as the description object to reveal the relations between concepts and the relations between the attributes of the concepts. Secnodly, the semantic standard concept was introduced to make the improvement to one step HMM. Lastly, the linear interpolation algorithm was used to compute the parameters of hidden Markov model based on semantic case amelioration. Finally pretty good experimental results have been achieved.