{'fraction_non_alphanumeric': 0.01371571072319202, 'fraction_numerical': 0.0, 'mean_word_length': 5.224806201550388, '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}}
How Well Can WordNet Measure Privacy: A Comparative Study?

Privacy is a fundamental issue in big data. Meanwhile, determining semantic relationships between words and phrases in privacy is required for effective privacy protection to the data that originates from a variety of sources, a main characteristic of big data. WordNet has been used as one of the most popular ways of measuring semantic similarity between words. In this paper, through comparison analysis, we show that WordNet is not very adequate for measuring semantic similarity or relatedness between words when concerning privacy. The analysis consists of an experiment to get human rating scores as the benchmark dataset and the comparison between results from WordNet based measures and the benchmark dataset to reach the conclusion.