Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning
Table 2. Quantitative results on the KBC task.
['[BOLD] Algorithm', '[BOLD] Wiki + Freebase  [BOLD] Hits@10', '[BOLD] Wiki + Freebase  [BOLD] MR', '[BOLD] NYT + Freebase  [BOLD] Hits@10', '[BOLD] NYT + Freebase  [BOLD] MR']
1. Knowledge Base Completion with Text Corpora (KBC). For the approach only considering the given seed instances (TransE), we see the performance is very limited due to the scarcity of seeds. Along the other line, the approach considering text corpora (word2vec) achieves relatively better results, but are still far from satisfactory, since it ignores the supervision from the seed instances. If we consider both the text corpus and seed instances for entity representation learning (RK), we obtain much better results. Moreover, by further jointly training a pattern model (DPE, CONV), the hits ratio can be further significantly improved.