{'fraction_non_alphanumeric': 0.019064124783362217, 'fraction_numerical': 0.0, 'mean_word_length': 6.410256410256411, '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}}
Learning Representations with Joint Models for Information Extraction

Unstructured natural language text contains vast quantities of human knowledge, yet this knowledge is mostly inaccessible to computers. Computers rely on structured representations (e.g. databases) for knowledge organization and retrieval, and cannot easily understand the ambiguity and nuance of human language. Dramatically increasing the accessibility of knowledge through search engines, interactive AI agents, and medical research tools requires extracting structured knowledge from unstructured text.