{'fraction_non_alphanumeric': 0.017985611510791366, 'fraction_numerical': 0.00539568345323741, 'mean_word_length': 5.329545454545454, '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}}
Tuning an Existing Nomenclature for Specific Domain Corpora: A Syntax-Based Similarity Method

There is a constant need to extend and tune medical vocabularies to account for new words and new word usages. Robust natural language processing (NLP) tools can be applied to medical texts corpora such as patient narratives and help collect and analyze unknown words1,2. The aim of the present work is to assess the potential for classifying unknown words based on the semantic categories of “neighbors” identified through syntactic distributional properties3.