Deeper Task-Specificity Improves Joint Entity and Relation Extraction
Table 2: Precision, Recall, and F1 scores for our model and other recent models on the ADE and CoNLL04 datasets. Because our scores are averaged across multiple trials, F1 scores shown here cannot be directly calculated from the precision and recall scores shown here. Note that Nguyen and Verspoor do not report precision and recall scores.
['Dataset', 'Model', 'Metric Type', 'NER Precision', 'NER Recall', 'NER F1', 'RE Precision', 'RE Recall', 'RE F1', 'Avg. F1']
In addition to precision, recall, and F1 scores for both tasks, we show the average of the F1 scores across both tasks. On the ADE dataset, we achieve SOTA results for both the NER and RE tasks. On the CoNLL04 dataset, we achieve SOTA results on the NER task, while our performance on the RE task is competitive with other recent models. On both datasets, we achieve SOTA results when considering the average F1 score across both tasks. The largest gain relative to the previous SOTA performance is on the RE task of the ADE dataset, where we see an absolute improvement of 4.5 on the macro-average F1 score.