Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping
Table 5: Experimental results for Japanese NER.
['[BOLD] NER  [BOLD] System', '[BOLD] Test (News)  [ITALIC] All', '[BOLD] Test (News)  [ITALIC] Unseen']
Again, in this low-resource case, DC(2) achieves the best improvement among the Wikipedia-based approaches. It improves the baseline by 9.0 F1 score on all the entities and by 18.3 F1 score on the unseen entities (59% of all the entities). The joint approach improves the baseline by 4.8 F1 score on all the entities and by 9.6 F1 score on the unseen entities.