{'fraction_non_alphanumeric': 0.01639344262295082, 'fraction_numerical': 0.0, 'mean_word_length': 5.395348837209302, '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}}
Detecting Story Analogies from Annotations of Time, Action and Agency

We describe the Story Intention Graph (SIG) as a model of narrative meaning that is amenable to both corpus annotation and computational inference. The relations, focusing on time, action and agency, can express a range of thematic scenarios and lend themselves to the automatic detection of story similarity and analogy. An evaluation finds that such detection outperforms a propositional similarity metric in predicting human judgments of story similarity in the Aesop domain.