Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers
Table 5: Strict, loose macro, and loose micro F1-scores on test sets
['[EMPTY]', 'Strict', 'L. Macro', 'L.Micro']
We provide these scores to give a better insight to researchers about ground-truths. It can be observed that the model trained with original TWNERTC performs poorly compared to post-processed versions. Since domain independent (DI) noise reduction method ensures that an entity can have only one type, it improves the performance more than the domain dependent (DD) method.