Abstract
This paper presents cross-lingual experiments in automatic detection of medical words that may be difficult to understand by patients. The study relies on Natural Language Processing (NLP) methods, conducted in three steps, across two languages, French and Xhosa: (1) the French data are processed by NLP methods and tools to reproduce the manual categorization of words as understandable or not; (2) the Xhosa data are clustered with a non-supervised algorithm; (3) an analysis of the Xhosa results and their comparison with the results observed on the French data is performed. Some similarities between the two languages are observed.
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