Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping
Table 6: Experimental results for Spanish, Dutch, and German NER.
['[BOLD] NER  [BOLD] System', '[BOLD] TestA  [ITALIC] All', '[BOLD] TestA  [ITALIC] Unseen', '[BOLD] TestB  [ITALIC] All', '[BOLD] TestB  [ITALIC] Unseen']
For each of the three languages, the baseline NER system is a CRF model trained with human-annotated news data (∼200K tokens), and there are two test data sets, TestA and TestB, that are also human-annotated news data (ranging from 40K to 70K tokens). For Dutch and German, DF(1) achieves the best improvement among the Wikipedia-based approaches. For Spanish, the joint approach achieves the best improvement among the Wikipedia-based approaches. Again, in all cases, the Wikipedia-based approaches demonstrate larger improvements (ranging from 1.0 to 3.4 F1 score) on the unseen entities.