{'fraction_non_alphanumeric': 0.016238159675236806, 'fraction_numerical': 0.005412719891745603, 'mean_word_length': 5.065573770491803, '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}}
Semantic Text Categorization using the K Nearest Neighbours Method

In this paper we investigate the influence of semantics in the text categorization. Moreover, we study how the vocabulary size reduction affects this task. The K Nearest Neighbours method was used to perform the categori- zation. In order to reduce the vocabulary size, the Information Gain technique was employed. A number of different document codification alternatives were tested. The experimental results for the best codifications were obtained taking into account the relevant terms and their synonyms for each text of the 20 Newsgroups and WebKB corpora. For the 20 Newsgroups corpus, the introduc- tion of semantics allowed for an improvement of the performance.