Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning
Table 1: Data labeling results using the dictionary: the number of labeled words (# of l.w.), the word-level precision (\# of true labeled words\# of total labeled words) and recall.
['Dataset', 'Type', '# of l.w.', 'Precision', 'Recall']
The resultant named entity dictionaries contain 2,000 person names, 748 location names, 353 organization names, and 104 MISC entities. From the table, we can see that the precision of the data labeling is acceptable but the recall is quite poor. This is expectable and is a typical problem of the method using only dictionaries to perform NER.