A matter of words: NLP for quality evaluation of Wikipedia medical articles
Table 3: Dictionary Composition
['semantic groups', 'definitions']
In details, we have pre-processed the entries by mean of the Tanl pipeline [tanl-pipeline], a suite of modules for text analytics and NLP, based on machine learning. Preprocessing has consisted in first dividing the entries into single word forms. Then, for each form, we have identified the lemma (when available) and the part of speech (POS). Thus, we have created an approximate definition that consists in using only the lemma and cleaning the text, excluding punctuation, prepositions and articles. Also, approximate definitions have been normalized by lowercasing each word. As an example, the Disorder entry “aneurysm of the vein of galen” has been stored in the dictionary, along with its approximate definition “aneurysm vein galen”.