Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping
Table 1: F1 score of English Wikipedia entity type classifiers.
['[BOLD] Features', '[BOLD] ALL', '[BOLD] PER', '[BOLD] ORG', '[BOLD] GPE', '[BOLD] TITL', '[BOLD] FAC']
To evaluate the prediction power of different types of features, we train a number of classifiers using only title features, only infobox features, only text features, and all features respectively. ALL is the overall performance, and PER (PERSON), ORG (ORGANIZATION), GPE, TITL (TITLEWORK), FAC (FACILITY) are the top five most frequently entity types in the test data. Nevertheless, both infobox and title features provide additional useful information for entity type classification, and the classifier trained with all the features achieves an overall F1 score of 90.1.