{'fraction_non_alphanumeric': 0.018518518518518517, 'fraction_numerical': 0.009259259259259259, 'mean_word_length': 5.142045454545454, '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}}
Nonlinearity of Concreteness and Semantic Depth: The Logistic Curve

Abstractness of a concept is viewed from two broad perspectives. Concreteness – the amount of sensory information in a concept and Precision – the amount of general information present in a concept. Though Concreteness and Precision is known to have been two different ways of measuring abstractness, the relationship between the two is not clear. This study proposes that the Mean Concreteness of words in a given Depth range (a measure of Precision) has a nonlinear relationship with Depth and can be modelled by a Generalized Logistic Function. The justification of the asymptotical upper and lower limit of Mean Concreteness in the Logistic model is based on the assumption of the ineffability of sensory information and embodied cognition. The proposed mathematical description based on generalized logistic function showed a good fit for the data collected from Brysbaert Dataset (R2 = 0.939) and was fairly accurate in predicting the Mean Concreteness scores in MRC psycholinguistic Dataset (R2 = 0.911).