{'fraction_non_alphanumeric': 0.03285420944558522, 'fraction_numerical': 0.0, 'mean_word_length': 5.873239436619718, '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}}
Towards robust multi-tool tagging: An ontology-based approach

In the realm of morphosyntactic annotations, ensemble combination techniques have been successfully applied to obtain more robust and more reliable linguistic analyses. Ensemble combination architectures employ multiple classifiers, e.g., part of speech taggers trained on different corpora, and combine their output, e.g., by means of a majority vote, phenomenondependent selection preferences or more elaborate techniques.