A matter of words: NLP for quality evaluation of Wikipedia medical articles
Table 5: Classification Results. In bold, the best results.
['[BOLD] Metric', '[BOLD] Baseline', '[BOLD] Medical  [BOLD] Domain', '[BOLD] Full Medical  [BOLD] Domain']
For each of the classes, we have computed the ROC Area and F-Measure metrics [powers2011evaluation]. The latter, in particular, is usually considered a significant metric in terms of classification, since it combines in one single value all the four indicators that are generally implied for evaluating the classifier performance (i.e., number of True Positives, False Positives, True Negatives and False Negatives). In our scenario, the meaning of the indicators, for each class, are as follows: True Positives are the articles classified as belonging to a certain class, that indeed belong to that class (according to the quality ratings given by the WikiProject Medicine); True Negatives are the articles classified as not belonging to a certain class, that indeed do not belong to that class; False Positives are the articles classified as belonging to a certain class, that do not belong to that class; False Negatives are the articles classified as not belonging to a certain class, that instead belong to that class. We notice also that exploiting the full medical domain leads us to the achievement of the best results.