{'fraction_non_alphanumeric': 0.012072434607645875, 'fraction_numerical': 0.0, 'mean_word_length': 4.928571428571429, '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}}
Intention Prediction in Search Engines Using Causal Bayesian Networks

This project will be taking sample data from search engine logs from Excite, Altavista, and Alltheweb. These logs will be parsed and a sorting algorithm will be used to associate words that are frequently used together to build a Causal Bayes’ Network. A database of images will be used to represent concepts tied to theses search terms and will be ranked with causal inference values to return an appropriately related image.