{'fraction_non_alphanumeric': 0.025815217391304348, 'fraction_numerical': 0.010869565217391304, 'mean_word_length': 5.522123893805309, '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}}
Clinical Acronym/Abbreviation Normalization using a Hybrid Approach

A unique characteristic of clinical text is the pervasive use of acronyms and abbreviations, which are often ambiguous. The ShARe/CLEF eHealth Evaluation Lab organized three shared tasks on clinical natural language processing (NLP) and information retrieval (IR) in 2013 and one of them was to normalize acronyms/abbreviations to UMLS concept unique identifiers (CUIs). This paper describes a hybrid system, which combines different Word Sense Disambiguation (WSD) methods and existing knowledge bases to normalize and encode clinical abbreviations. Our system achieved the best accuracy of 0.719 on the independent test set, which was ranked first in the challenge.