Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers
Table 4: Evaluation for Coarse-Grained TWNERTC, TWNERTC-DI and TWNERTC-DD
['[EMPTY]', 'Precision', 'Recall', 'F1-Score']
As a conclusion, among the automatically annotated coarse-grained entity types, there are %76 matching ratio without O tags and manually added types. The dataset with domain-dependent post-process provides better NER performance compared to other two versions in general. Moreover, both post-processing methods improve the performance compared to the original dataset. Further, it can be observed that misc type is has the lowest F-Score among all types in all versions; however, this result is expected since misc’s coverage is larger than the other three types and makes it more vulnerable to mismatches.