Multi-class Multilingual Classification of Wikipedia ArticlesUsing Extended Named Entity Tag Set
Table 2: The classification accuracy of the predicted labels. Partially correct labels have also contributed partially to the scores. † Despite our endeavor to keep the settings comparable to the original model, comparison between our results and theirs would not be fair, since the size of datasets used in our experiments and also the number of classes are different than theirs.
['Model', 'dev ja', 'dev en', 'dev de', 'dev fr', 'dev fa', 'test ja', 'test en', 'test de', 'test fr', 'test fa']
The results initially demonstrate that the dataset is not an easy one as the Binary Logistic Regression model is not achieving very high accuracy scores. Besides, the lower scores for Japanese in comparison to the other languages demonstrate the higher difficulty level of classification for a larger category set size for all the models.