Leveraging 2-hop Distant Supervision from Table Entity Pairs for Relation Extraction
Table 4: Comparison on Precision@recall and AUC under different testing settings, detailed in Section 4.4.3.
['Test Mode', 'SINGLE', 'SINGLE', 'SINGLE', 'SINGLE', 'MULTIPLE ONE', 'MULTIPLE ONE', 'MULTIPLE ONE', 'MULTIPLE ONE', 'MULTIPLE TWO', 'MULTIPLE TWO', 'MULTIPLE TWO', 'MULTIPLE TWO', 'MULTIPLE ALL', 'MULTIPLE ALL', 'MULTIPLE ALL', 'MULTIPLE ALL']
In the originally testing set, there are 79176 entity pairs that are associated with only one sentence, out of which 1149 actually have relations. We hope our model can improve performance on these long-tail entities. Following Lin et al. We further construct the “ONE” testing setting where we randomly select one sentence for each entity pair; the “TWO” setting where we randomly select two sentences for each entity pair and the “ALL” setting where we use all the associated sentences from MULTIPLE. We use all sentences in ST for each entity pair if it is nonempty. Although the performance of all models generally improves as the sentence number increases in MULTIPLE setting, models leveraging 2-hop DS are more stable and have smaller changes. These observations indicate that 2-hop DS is helpful when information obtained by 1-hop DS is insufficient.