{'fraction_non_alphanumeric': 0.022443890274314215, 'fraction_numerical': 0.006234413965087282, 'mean_word_length': 5.129770992366412, '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}}
Towards the Prepositional Meaning via Machine Learning: A Case Study of Spanish Grammar

Is it possible to identify or measure prepositional meaning? In our article we review a particular case of semantic universe, the verbs of movement in Spanish. In this context, we try to answer positively the initial question and validate a method. From the selection of a corpus of 71,206 prepositional phrases in Spanish, where three prepositions – a, hacia and hasta – are distributed, we proceed to verify the hypothesis about the semantic gradualness of the prepositions (HGSS). Applying tools of the field of machine learning, we establish a series of groupings that are compared with the hand annotation classification. The results are statistically relevant insofar as they confirm our initial hypothesis.