Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping
Table 2: Improving classifier accuracy via self-training.
['[BOLD] Classifier', '[BOLD] Train Size', '[BOLD] F1']
We first apply a standard self-training approach. The classifier trained with the initial training data is used to decode (i.e., classify) all the unlabeled Wikipedia pages to predict their entity types with confidence scores. We add the self-decoded Wikipedia pages with high confidence scores to the training data and train a new classifier. Via experiments a threshold of 0.9 is used to sort out high-confident self-decoded examples. To address this issue, we have developed a sampling-based self-training approach. Instead of adding all the self-decoded examples with confidence scores greater than or equal to 0.9, we do a random sampling of those high-confident examples. We use a sampling probability p(e)=q⋅c(e), where q is a sampling ratio parameter and c(e) is the confidence score of example e. Under this approach, examples with higher confidence scores are more likely to be selected, while the total number of selected examples is controlled by the sampling ratio q. Via experiments we found that a small sampling ratio like q=0.01 yields good improvement (although the improvement is not sensitive to q).