Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning
Table 3: P@N for relation extraction in the entity pairs with different number of sentences
['Test Settings P@N', 'One 100', 'One 200', 'One 300', 'One Mean', 'Two 100', 'Two 200', 'Two 300', 'Two Mean', 'All 100', 'All 200', 'All 300', 'All Mean']
Following previous works, we adopt P@N as a quantitative indicator to compare our model with baselines based on various instances under each relational tuple. We can find: (1) Compared with baselines, BGRU+STP+EWA+TL achieves the best performance in all test settings, which increases the performance of PCNN+ATT in three settings by 6.3%, 7.6%, and 7.7% respectively. It demonstrates that the integrated model is the most effective; (2) Our STP and entity-wise attention combined with word-level attention reduce inner-sentence noise effectively, and outperform baselines by over 5%; (3) Our neural extractor initialized with a priori knowledge learned from entity type classification is more robust against word-level noise where BGRU+STP+EWA+TL has an improvement of 2% over BGRU+STP+EWA.