{'fraction_non_alphanumeric': 0.018318965517241378, 'fraction_numerical': 0.0, 'mean_word_length': 6.037878787878788, '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}}
A Knowledge Framework for Natural Language Analysis

Recent research in language analysis and language generation has highlighted the role of knowledge representation in both processes. Certain knowledge representation foundations, such as structured inheritance networks and feature-based linguistic representations, have proved useful in a variety of language processing tasks. Augmentations to this common framework, however, are required to handle particular issues, such as the ROLE RELATIONSHIP problem: the task of determining how roles, or slots, of a given frame, are filled based on knowledge about other roles. Three knowledge structures are discussed that address this problem. The semantic interpreter of an analyzer called TRUMP (TRansportable Understanding Mechanism Package) uses these structures to determine the fillers of roles effectively without requiring excessive specialized information about each frame.