{'fraction_non_alphanumeric': 0.02564102564102564, 'fraction_numerical': 0.0, 'mean_word_length': 5.625, '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}}
Linking Text and Knowledge Using the INCEpTION Annotation Platform

Abstract-In the Digital Humanities (DH), linking text collections to general or domain-specific knowledge bases (KBs) or authority files is important to enable a contextualised analysis. Automatic named entity recognition and entity linking tools require training data or domain-specific methods. Interactive annotation tools do often not support the tasks of entity linking, fact-linking, cross-document reference resolution, etc. We aim to address this gap with the INCEpTION annotation platform, which not only provides these capabilities in the context of a generic annotation tool, but also combines them with machine learning methods to improve annotation efficiency.