Towards Time-Aware Distant Supervisionfor Relation Extraction
Table 3. Macro-average of precision, recall and F1 of relation classification model trained on Original Set with curriculum learning.
['[BOLD] Training Set', '[BOLD] macro-P(%)', '[BOLD] macro-R(%)', '[BOLD] macro-F1(%)']
We find that (1) curriculum learning based Time-DS significantly outperform the basic DS in any training round; (2) Each training round of curriculum learning outperform the traditional training in hard filter strategy. (1) From the comparison between the different rounds of Time-DS with curriculum learning and the basic DS, it is clear that Time-DS with curriculum learning outperforms the basic DS in every round of training and Time-DS with curriculum learning achieves the best performance at round 4. (2) From round 1 to round 4, the noisy samples are gradually added, the performance tends to increase. However, the performance decrease when adding to much noisy samples into the training set after round 4.