Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping
Table 4: Experimental results for Portuguese NER.
['[BOLD] NER  [BOLD] System', '[BOLD] Test (News)  [ITALIC] All', '[BOLD] Test (News)  [ITALIC] Unseen']
Because the system is trained with little human-annotated training data, the performance of the baseline system achieves only 60.1 F1 score on all the entities and 50.2 F1 score on the unseen entities (80% of all the entities). In this case, the more aggressive decoding constraint approach DC(2) achieves the best improvement among the Wikipedia-based approaches, which improves the baseline by 5.9 F1 score on all the entities and by 8.6 F1 score on the unseen entities. The joint approach improves the baseline by 3.0 F1 score on all the entities and by 4.3 F1 score on the unseen entities.